#IMPORT ALL THE NECESSARY LIBRARIES INTO THE NOTEBOOK.
import tensorflow as tf
import numpy as np
import math
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, LeakyReLU
from keras.optimizers import SGD, Adadelta
from keras import optimizers
from sklearn import metrics
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pickle
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras import regularizers, optimizers
from sklearn.metrics import r2_score
from tensorflow.keras.models import load_model
import warnings
warnings.filterwarnings("ignore")
import random
seed = 7
np.random.seed(seed)
SIGNAL_DATA = pd.read_csv('NN Project Data - Signal.csv')
DATA_COPY = SIGNAL_DATA.copy()
SIGNAL_DATA.head()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
SIGNAL_DATA.tail()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1594 | 6.2 | 0.600 | 0.08 | 2.0 | 0.090 | 32.0 | 44.0 | 0.99490 | 3.45 | 0.58 | 10.5 | 5 |
| 1595 | 5.9 | 0.550 | 0.10 | 2.2 | 0.062 | 39.0 | 51.0 | 0.99512 | 3.52 | 0.76 | 11.2 | 6 |
| 1596 | 6.3 | 0.510 | 0.13 | 2.3 | 0.076 | 29.0 | 40.0 | 0.99574 | 3.42 | 0.75 | 11.0 | 6 |
| 1597 | 5.9 | 0.645 | 0.12 | 2.0 | 0.075 | 32.0 | 44.0 | 0.99547 | 3.57 | 0.71 | 10.2 | 5 |
| 1598 | 6.0 | 0.310 | 0.47 | 3.6 | 0.067 | 18.0 | 42.0 | 0.99549 | 3.39 | 0.66 | 11.0 | 6 |
SIGNAL_DATA.shape
(1599, 12)
SIGNAL_DATA.info()
print('\n\n',SIGNAL_DATA['Signal_Strength'].unique())
<class 'pandas.core.frame.DataFrame'> Int64Index: 1359 entries, 0 to 1598 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Parameter 1 1359 non-null float64 1 Parameter 2 1359 non-null float64 2 Parameter 3 1359 non-null float64 3 Parameter 4 1359 non-null float64 4 Parameter 5 1359 non-null float64 5 Parameter 6 1359 non-null float64 6 Parameter 7 1359 non-null float64 7 Parameter 8 1359 non-null float64 8 Parameter 9 1359 non-null float64 9 Parameter 10 1359 non-null float64 10 Parameter 11 1359 non-null float64 11 Signal_Strength 1359 non-null int64 dtypes: float64(11), int64(1) memory usage: 170.3 KB [5 6 7 4 8 3]
SIGNAL_DATA.describe()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 |
| mean | 8.319637 | 0.527821 | 0.270976 | 2.538806 | 0.087467 | 15.874922 | 46.467792 | 0.996747 | 3.311113 | 0.658149 | 10.422983 | 5.636023 |
| std | 1.741096 | 0.179060 | 0.194801 | 1.409928 | 0.047065 | 10.460157 | 32.895324 | 0.001887 | 0.154386 | 0.169507 | 1.065668 | 0.807569 |
| min | 4.600000 | 0.120000 | 0.000000 | 0.900000 | 0.012000 | 1.000000 | 6.000000 | 0.990070 | 2.740000 | 0.330000 | 8.400000 | 3.000000 |
| 25% | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 5.000000 |
| 50% | 7.900000 | 0.520000 | 0.260000 | 2.200000 | 0.079000 | 14.000000 | 38.000000 | 0.996750 | 3.310000 | 0.620000 | 10.200000 | 6.000000 |
| 75% | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997835 | 3.400000 | 0.730000 | 11.100000 | 6.000000 |
| max | 15.900000 | 1.580000 | 1.000000 | 15.500000 | 0.611000 | 72.000000 | 289.000000 | 1.003690 | 4.010000 | 2.000000 | 14.900000 | 8.000000 |
* WE HAVE A TOTAL OF 12 COLUMNS IN THE DATA SET.
* ALL THE COLUMNS ARE OF NUMERIC DATA TYPE.
* THERA RE 1599 ROWS OF DATA IN THE IMPORTED FILE.
* SIGNAL_STRENGTH COLUMN VALUE IS SEEMS TO BE DEPENDENT ON THE PARAMETER 1 TO 11.
* FROM THE DATA DESCRIPTION, IT SEEMS THAT THERE ARE NO NULL VALUES IN THE DATA.
* PARAMETER 7 SEEMS TO BE SKEWED HEAVILY TO RIGHT FOLLOWED BY PARAMETER 6 AND PARAMETER 4.
ISNULL = SIGNAL_DATA.isnull().sum().values.sum()
if ISNULL==0:
print('\n\nNO NULL VALUES ARE PRESENT IN THE DATA')
else:
print('\n\nTHERE ARE NULL VALUES PRESENT IN THE DATA')
NO NULL VALUES ARE PRESENT IN THE DATA
* THERE ARE NO MISSING VALUES IN THE DATA.
ISDUP = SIGNAL_DATA.duplicated().sum()
if ISDUP==0:
print('\nNO DUPLICATE VALUES ARE PRESENT IN THE DATA')
else:
print('\nTHERE ARE', ISDUP ,'DUPLICATE VALUES PRESENT IN THE DATA')
THERE ARE 240 DUPLICATE VALUES PRESENT IN THE DATA
SIGNAL_DATA.drop_duplicates(keep='first', inplace=True)
SIGNAL_DATA.shape
(1359, 12)
* THERE ARE 240 DUPLICATE VALUES IN THE DATA WHICH IS ALMOST 15% OF THE DATA.
* SINCE THE DATA IS ABOUT THE SIGNAL STRENGTH AND QUALITY AND DATA IS NUMERIC, WE CAN KEEP ONLY 1ST ROW
AND DROP REMAINING ROWS TO HAVE ONLY UNIQUE ROWS IN THE DATA.
fig, ax = plt.subplots(3,4 , sharex=True, figsize=(16,16))
x=0
y=0
COLS = []
X = np.arange(0,11,1)
for i in X:
COLS.append(SIGNAL_DATA.columns[i])
for i in COLS:
sns.stripplot(y = SIGNAL_DATA[i], x = SIGNAL_DATA['Signal_Strength'],ax = ax[x, y],
palette="Set2", hue = SIGNAL_DATA['Signal_Strength'])
y = y+1
if y == 4:
x=x+1
y=0
plt.show()
sns.pairplot(SIGNAL_DATA, hue = "Signal_Strength", diag_kind = "kde",kind = "scatter",
markers=["o", "s", "D","o", "s", "D"],palette = "Set2")
plt.show()
plt.figure(figsize=(8,8))
ax = sns.heatmap(SIGNAL_DATA.corr(method = 'pearson'), annot = True, linewidths = 1, square = True, cmap="YlOrRd")
plt.title("SIGNAL DATA CORRELATION HEAT MAP")
plt. show()
* SIGNAL STRENGTH OF THE SIGNALS RANGES BETWEEN 3 - 8
* FOR MOST OF THE SIGNALS STRENGTH IS LYING BETWEEN 5 - 7
* THERE IS NEGATIVE VALUES IN CORRELATION MATRIX INDICATING WEAK CORRELATION
* THERE ARE 240 DUPLICATE ROWS PRESENT WHICH WE HAVE DROPPED FROM THE DATA SET BY KEEPING ONLY FIRST ROW
* THERE ARE PRESENSE OF OUTLIERS IN SOME OF THE SIGNAL PARAMETERS.
# DEFINE TWO VARIABLES X AND Y AND ASSIGN INDEPENDENT VARIABLES TO X AND TARGET VARIABLE TO Y
X = SIGNAL_DATA.drop("Signal_Strength", axis=1)
Y = SIGNAL_DATA['Signal_Strength']
X.head()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 |
| 5 | 7.4 | 0.66 | 0.00 | 1.8 | 0.075 | 13.0 | 40.0 | 0.9978 | 3.51 | 0.56 | 9.4 |
Y.unique()
array([5, 6, 7, 4, 8, 3], dtype=int64)
# SPLIT THE DATA INTO TRAINING AND TESTING WITH TRAINING SIZE OF 70% AND TESTING DATA SIZE OF 30%
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=seed)
print('SHAPE OF X_TRAIN: ',x_train.shape)
print('\nSHAPE OF X_TEST: ',x_test.shape)
print('\nSHAPE OF Y_TRAIN: ',y_train.shape[0])
print('\nSHAPE OF Y_TEST: ',y_test.shape[0])
SHAPE OF X_TRAIN: (951, 11) SHAPE OF X_TEST: (408, 11) SHAPE OF Y_TRAIN: 951 SHAPE OF Y_TEST: 408
SIGNAL_DATA['Signal_Strength'].value_counts()
5 577 6 535 7 167 4 53 8 17 3 10 Name: Signal_Strength, dtype: int64
fig, ax = plt.subplots(2,2 , sharex=True, figsize=(10,10))
sns.distplot(Y,ax = ax[0,0])
sns.distplot(y_train,ax = ax[0,1])
sns.distplot(y_test,ax = ax[1,0])
fig.tight_layout()
plt.show()
* TARGET VARIABLES Y, Y_TRAIN AND Y_TEST SEEMS TO FOLLOW SAME DISTRIBUTION IN THE PLOT
X_S = StandardScaler().fit_transform(X)
x_train_s = StandardScaler().fit_transform(x_train)
x_test_s = StandardScaler().fit_transform(x_test)
y_c = to_categorical(Y)
y_train_c = to_categorical(y_train)
y_test_c = to_categorical(y_test)
y_train_c[1]
array([0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)
NN_MODEL = Sequential()
NN_MODEL.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='relu'))
NN_MODEL.add(tf.keras.layers.BatchNormalization())
NN_MODEL.add(Dense(128, kernel_initializer='normal', activation='relu'))
NN_MODEL.add(Dense(128, kernel_initializer='normal'))
NN_MODEL.add(LeakyReLU(alpha=0.1))
NN_MODEL.add(Dense(9, activation ="softmax"))
NN_MODEL.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
NN_HISTORY = NN_MODEL.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 3s 14ms/step - loss: 1.4682 - accuracy: 0.5142 - val_loss: 1.7334 - val_accuracy: 0.5833 Epoch 2/100 96/96 [==============================] - 1s 10ms/step - loss: 1.0710 - accuracy: 0.5762 - val_loss: 1.4708 - val_accuracy: 0.6078 Epoch 3/100 96/96 [==============================] - 1s 9ms/step - loss: 1.0193 - accuracy: 0.5931 - val_loss: 1.2391 - val_accuracy: 0.6029 Epoch 4/100 96/96 [==============================] - 1s 9ms/step - loss: 0.9685 - accuracy: 0.6036 - val_loss: 1.0904 - val_accuracy: 0.5980 Epoch 5/100 96/96 [==============================] - 1s 10ms/step - loss: 0.9478 - accuracy: 0.6025 - val_loss: 1.0060 - val_accuracy: 0.5858 Epoch 6/100 96/96 [==============================] - 1s 9ms/step - loss: 0.9112 - accuracy: 0.6351 - val_loss: 1.0127 - val_accuracy: 0.5686 Epoch 7/100 96/96 [==============================] - 1s 9ms/step - loss: 0.9011 - accuracy: 0.6246 - val_loss: 1.0002 - val_accuracy: 0.5686 Epoch 8/100 96/96 [==============================] - 1s 9ms/step - loss: 0.9022 - accuracy: 0.6078 - val_loss: 0.9998 - val_accuracy: 0.5784 Epoch 9/100 96/96 [==============================] - 1s 9ms/step - loss: 0.8722 - accuracy: 0.6435 - val_loss: 0.9875 - val_accuracy: 0.5760 Epoch 10/100 96/96 [==============================] - 1s 9ms/step - loss: 0.8862 - accuracy: 0.6362 - val_loss: 0.9945 - val_accuracy: 0.5735 Epoch 11/100 96/96 [==============================] - 1s 9ms/step - loss: 0.8594 - accuracy: 0.6519 - val_loss: 1.0115 - val_accuracy: 0.5735 Epoch 12/100 96/96 [==============================] - 1s 9ms/step - loss: 0.8384 - accuracy: 0.6677 - val_loss: 1.0440 - val_accuracy: 0.5637 Epoch 13/100 96/96 [==============================] - 1s 13ms/step - loss: 0.8221 - accuracy: 0.6677 - val_loss: 1.0595 - val_accuracy: 0.5784 Epoch 14/100 96/96 [==============================] - 1s 12ms/step - loss: 0.8256 - accuracy: 0.6719 - val_loss: 1.0355 - val_accuracy: 0.5858 Epoch 15/100 96/96 [==============================] - 1s 12ms/step - loss: 0.8079 - accuracy: 0.6593 - val_loss: 1.0478 - val_accuracy: 0.5882 Epoch 16/100 96/96 [==============================] - 1s 13ms/step - loss: 0.7950 - accuracy: 0.6698 - val_loss: 1.0679 - val_accuracy: 0.5662 Epoch 17/100 96/96 [==============================] - 1s 12ms/step - loss: 0.7876 - accuracy: 0.6709 - val_loss: 1.0422 - val_accuracy: 0.6054 Epoch 18/100 96/96 [==============================] - 1s 12ms/step - loss: 0.7685 - accuracy: 0.6856 - val_loss: 1.1273 - val_accuracy: 0.5711 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7856 - accuracy: 0.6625 - val_loss: 1.1084 - val_accuracy: 0.5588 Epoch 20/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7732 - accuracy: 0.6709 - val_loss: 1.1153 - val_accuracy: 0.5539 Epoch 21/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7744 - accuracy: 0.6740 - val_loss: 1.0881 - val_accuracy: 0.5931 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7454 - accuracy: 0.6824 - val_loss: 1.0897 - val_accuracy: 0.5907 Epoch 23/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7460 - accuracy: 0.6919 - val_loss: 1.0816 - val_accuracy: 0.5980 Epoch 24/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7149 - accuracy: 0.7024 - val_loss: 1.1470 - val_accuracy: 0.5319 Epoch 25/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7431 - accuracy: 0.6877 - val_loss: 1.1054 - val_accuracy: 0.5490 Epoch 26/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7140 - accuracy: 0.7108 - val_loss: 1.2503 - val_accuracy: 0.4975 Epoch 27/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7130 - accuracy: 0.6993 - val_loss: 1.1380 - val_accuracy: 0.5686 Epoch 28/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6931 - accuracy: 0.7171 - val_loss: 1.1499 - val_accuracy: 0.5662 Epoch 29/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6988 - accuracy: 0.7045 - val_loss: 1.1527 - val_accuracy: 0.5637 Epoch 30/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6929 - accuracy: 0.7098 - val_loss: 1.1574 - val_accuracy: 0.5490 Epoch 31/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6650 - accuracy: 0.7203 - val_loss: 1.1850 - val_accuracy: 0.5515 Epoch 32/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6768 - accuracy: 0.7266 - val_loss: 1.1484 - val_accuracy: 0.5784 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6922 - accuracy: 0.7129 - val_loss: 1.1513 - val_accuracy: 0.5907 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6646 - accuracy: 0.7256 - val_loss: 1.2685 - val_accuracy: 0.5343 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6491 - accuracy: 0.7224 - val_loss: 1.1915 - val_accuracy: 0.5637 Epoch 36/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6248 - accuracy: 0.7424 - val_loss: 1.1834 - val_accuracy: 0.6005 Epoch 37/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6242 - accuracy: 0.7319 - val_loss: 1.1783 - val_accuracy: 0.5637 Epoch 38/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6100 - accuracy: 0.7560 - val_loss: 1.2599 - val_accuracy: 0.5539 Epoch 39/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6123 - accuracy: 0.7592 - val_loss: 1.3355 - val_accuracy: 0.4828 Epoch 40/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5977 - accuracy: 0.7592 - val_loss: 1.3642 - val_accuracy: 0.4755 Epoch 41/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5830 - accuracy: 0.7529 - val_loss: 1.2399 - val_accuracy: 0.5711 Epoch 42/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5663 - accuracy: 0.7729 - val_loss: 1.2173 - val_accuracy: 0.5931 Epoch 43/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6037 - accuracy: 0.7445 - val_loss: 1.2022 - val_accuracy: 0.5686 Epoch 44/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5600 - accuracy: 0.7729 - val_loss: 1.2667 - val_accuracy: 0.5588 Epoch 45/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5520 - accuracy: 0.7739 - val_loss: 1.2976 - val_accuracy: 0.5711 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5394 - accuracy: 0.7876 - val_loss: 1.4271 - val_accuracy: 0.5123 Epoch 47/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5476 - accuracy: 0.7855 - val_loss: 1.2100 - val_accuracy: 0.5956 Epoch 48/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5435 - accuracy: 0.7676 - val_loss: 1.3608 - val_accuracy: 0.5564 Epoch 49/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5121 - accuracy: 0.8044 - val_loss: 1.3503 - val_accuracy: 0.5441 Epoch 50/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5316 - accuracy: 0.7792 - val_loss: 1.4015 - val_accuracy: 0.5637 Epoch 51/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5395 - accuracy: 0.7865 - val_loss: 1.4024 - val_accuracy: 0.5294 Epoch 52/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5327 - accuracy: 0.8034 - val_loss: 1.4588 - val_accuracy: 0.5588 Epoch 53/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5137 - accuracy: 0.8002 - val_loss: 1.3823 - val_accuracy: 0.5098 Epoch 54/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4966 - accuracy: 0.8076 - val_loss: 1.4517 - val_accuracy: 0.5319 Epoch 55/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4791 - accuracy: 0.8107 - val_loss: 1.4931 - val_accuracy: 0.5123 Epoch 56/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5131 - accuracy: 0.7971 - val_loss: 1.4638 - val_accuracy: 0.5392 Epoch 57/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4942 - accuracy: 0.7928 - val_loss: 1.4919 - val_accuracy: 0.5294 Epoch 58/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4688 - accuracy: 0.8233 - val_loss: 1.3815 - val_accuracy: 0.5613 Epoch 59/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5116 - accuracy: 0.7834 - val_loss: 1.4883 - val_accuracy: 0.5466 Epoch 60/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4402 - accuracy: 0.8297 - val_loss: 1.6659 - val_accuracy: 0.4975 Epoch 61/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4630 - accuracy: 0.8191 - val_loss: 1.5687 - val_accuracy: 0.5368 Epoch 62/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4539 - accuracy: 0.8139 - val_loss: 1.4624 - val_accuracy: 0.5613 Epoch 63/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4637 - accuracy: 0.8275 - val_loss: 1.6258 - val_accuracy: 0.5368 Epoch 64/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4798 - accuracy: 0.8044 - val_loss: 1.5641 - val_accuracy: 0.5466 Epoch 65/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4485 - accuracy: 0.8244 - val_loss: 1.5530 - val_accuracy: 0.5147 Epoch 66/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4153 - accuracy: 0.8297 - val_loss: 1.5977 - val_accuracy: 0.5564 Epoch 67/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4426 - accuracy: 0.8349 - val_loss: 1.4482 - val_accuracy: 0.5441 Epoch 68/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4280 - accuracy: 0.8370 - val_loss: 1.5134 - val_accuracy: 0.5735 Epoch 69/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4411 - accuracy: 0.8286 - val_loss: 1.6183 - val_accuracy: 0.5588 Epoch 70/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4289 - accuracy: 0.8244 - val_loss: 1.5456 - val_accuracy: 0.5392 Epoch 71/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4355 - accuracy: 0.8339 - val_loss: 1.6515 - val_accuracy: 0.5270 Epoch 72/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4413 - accuracy: 0.8181 - val_loss: 1.5359 - val_accuracy: 0.5368 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4247 - accuracy: 0.8370 - val_loss: 1.4394 - val_accuracy: 0.5637 Epoch 74/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3961 - accuracy: 0.8402 - val_loss: 1.6826 - val_accuracy: 0.5515 Epoch 75/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3804 - accuracy: 0.8507 - val_loss: 1.5997 - val_accuracy: 0.5588 Epoch 76/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4034 - accuracy: 0.8339 - val_loss: 1.5325 - val_accuracy: 0.5662 Epoch 77/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4009 - accuracy: 0.8402 - val_loss: 1.5561 - val_accuracy: 0.5564 Epoch 78/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3813 - accuracy: 0.8549 - val_loss: 1.6884 - val_accuracy: 0.5343 Epoch 79/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3974 - accuracy: 0.8339 - val_loss: 1.6762 - val_accuracy: 0.5441 Epoch 80/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3713 - accuracy: 0.8633 - val_loss: 1.5586 - val_accuracy: 0.5686 Epoch 81/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3882 - accuracy: 0.8423 - val_loss: 1.6198 - val_accuracy: 0.5466 Epoch 82/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3737 - accuracy: 0.8591 - val_loss: 1.6243 - val_accuracy: 0.5539 Epoch 83/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4208 - accuracy: 0.8328 - val_loss: 1.5724 - val_accuracy: 0.5221 Epoch 84/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3778 - accuracy: 0.8580 - val_loss: 1.7754 - val_accuracy: 0.5466 Epoch 85/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3813 - accuracy: 0.8486 - val_loss: 1.7517 - val_accuracy: 0.5196 Epoch 86/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3793 - accuracy: 0.8465 - val_loss: 1.8277 - val_accuracy: 0.5392 Epoch 87/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3279 - accuracy: 0.8728 - val_loss: 1.7019 - val_accuracy: 0.5441 Epoch 88/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3479 - accuracy: 0.8791 - val_loss: 1.7872 - val_accuracy: 0.5490 Epoch 89/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3338 - accuracy: 0.8675 - val_loss: 1.6949 - val_accuracy: 0.5515 Epoch 90/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3630 - accuracy: 0.8601 - val_loss: 1.6855 - val_accuracy: 0.5123 Epoch 91/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3451 - accuracy: 0.8644 - val_loss: 1.6823 - val_accuracy: 0.5123 Epoch 92/100 96/96 [==============================] - 0s 5ms/step - loss: 0.3409 - accuracy: 0.8633 - val_loss: 1.9215 - val_accuracy: 0.5074 Epoch 93/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3115 - accuracy: 0.8707 - val_loss: 1.6684 - val_accuracy: 0.5319 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3555 - accuracy: 0.8528 - val_loss: 1.6717 - val_accuracy: 0.5466 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3229 - accuracy: 0.8780 - val_loss: 1.6809 - val_accuracy: 0.5441 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3245 - accuracy: 0.8770 - val_loss: 1.8043 - val_accuracy: 0.5270 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3135 - accuracy: 0.8833 - val_loss: 1.7612 - val_accuracy: 0.5294 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3277 - accuracy: 0.8696 - val_loss: 1.7947 - val_accuracy: 0.5515 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3159 - accuracy: 0.8791 - val_loss: 1.8920 - val_accuracy: 0.5147 Epoch 100/100 96/96 [==============================] - 1s 5ms/step - loss: 0.3346 - accuracy: 0.8696 - val_loss: 1.7488 - val_accuracy: 0.5613
HIST_DF = pd.DataFrame.from_dict(NN_HISTORY.history)
print(HIST_DF.sort_values('val_accuracy',ascending = False))
loss accuracy val_loss val_accuracy 1 1.071029 0.576236 1.470840 0.607843 16 0.787572 0.670873 1.042171 0.605392 2 1.019254 0.593060 1.239072 0.602941 35 0.624774 0.742376 1.183446 0.600490 3 0.968546 0.603575 1.090435 0.598039 .. ... ... ... ... 91 0.340908 0.863302 1.921546 0.507353 59 0.440217 0.829653 1.665873 0.497549 25 0.714044 0.710831 1.250252 0.497549 38 0.612255 0.759201 1.335481 0.482843 39 0.597704 0.759201 1.364223 0.475490 [100 rows x 4 columns]
TRAINING_LOSS = HIST_DF['loss']
VALIDATION_LOSS = HIST_DF['val_loss']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_LOSS, 'r', label='TRAINING_LOSS')
plt.plot(EPOCHS, VALIDATION_LOSS, 'b', label='VALIDATION_LOSS')
plt.title('TRAINING AND VALIDATION LOSS COMPARISON PLOT')
plt.xlabel('EPOCHS')
plt.ylabel('LOSS')
plt.legend()
plt.show()
TRAINING_ACC = HIST_DF['accuracy']
VALIDATION_ACC = HIST_DF['val_accuracy']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_ACC, 'r', label='TRAINING_ACCURACY')
plt.plot(EPOCHS, VALIDATION_ACC, 'b', label='VALIDATION_ACCURACY')
plt.title('TRAINING AND VALIDATION ACCURACY COMPARISON PLOT')
plt.xlabel('EPOCHS')
plt.ylabel('ACCURACY')
plt.legend()
plt.show()
# TRY INCREASING THE EPOCHS COUNT AND CHECK THE PERFORMANCE.
RESULTS = pd.DataFrame()
for i in np.arange(200,501,100):
EPOCH_HIST = NN_MODEL.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=i, batch_size=10).history
y_pred = NN_MODEL.predict(x_test_s)
SCORE = metrics.r2_score(y_test_c,y_pred)
MAX_ACC = np.max(EPOCH_HIST['accuracy'])
MAX_VAL_ACC = np.max(EPOCH_HIST['val_accuracy'])
RESULTS = RESULTS.append(pd.Series([i,SCORE,MAX_ACC,MAX_VAL_ACC]),ignore_index=True)
RESULTS.columns = ['NO OF EPOCHS','MODEL SCORE','TRAINING ACCURACY','TESTING ACCURACY']
Epoch 1/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4164 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 2/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2239 - accuracy: 0.4269 - val_loss: 1.1872 - val_accuracy: 0.3652 Epoch 3/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4185 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 4/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4069 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 5/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4196 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 6/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4101 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 7/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3912 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 8/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3891 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 9/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4059 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 10/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4059 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 11/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4038 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 12/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3985 - val_loss: 1.1855 - val_accuracy: 0.3652 Epoch 13/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3933 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 14/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.4080 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 15/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3996 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 16/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.4017 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 17/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3922 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 18/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3670 - val_loss: 1.1813 - val_accuracy: 0.3652 Epoch 19/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2270 - accuracy: 0.3985 - val_loss: 1.1738 - val_accuracy: 0.4706 Epoch 20/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4017 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 21/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 22/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3817 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 23/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4090 - val_loss: 1.1646 - val_accuracy: 0.4706 Epoch 24/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.4038 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 25/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4143 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 26/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3775 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 27/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3922 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 28/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3943 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 29/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4027 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 30/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4017 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 31/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3880 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 32/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4101 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 33/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4017 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 34/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4101 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 35/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2261 - accuracy: 0.4080 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 36/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.3954 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 37/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4090 - val_loss: 1.1826 - val_accuracy: 0.3652 Epoch 38/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4059 - val_loss: 1.2021 - val_accuracy: 0.3652 Epoch 39/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4132 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 40/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4059 - val_loss: 1.1781 - val_accuracy: 0.4706 Epoch 41/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2266 - accuracy: 0.3680 - val_loss: 1.1885 - val_accuracy: 0.3652 Epoch 42/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.4090 - val_loss: 1.1861 - val_accuracy: 0.3652 Epoch 43/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4048 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 44/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2256 - accuracy: 0.3954 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 45/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3964 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 46/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2262 - accuracy: 0.4048 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 47/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3817 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 48/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2261 - accuracy: 0.3954 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 49/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2270 - accuracy: 0.3775 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 50/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2230 - accuracy: 0.4332 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 51/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4185 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 52/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2242 - accuracy: 0.4301 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 53/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2225 - accuracy: 0.4322 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 54/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2280 - accuracy: 0.3933 - val_loss: 1.1737 - val_accuracy: 0.4706 Epoch 55/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2257 - accuracy: 0.3891 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 56/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4343 - val_loss: 1.1899 - val_accuracy: 0.3652 Epoch 57/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4017 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 58/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2243 - accuracy: 0.4090 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 59/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2266 - accuracy: 0.3901 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 60/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4017 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 61/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3933 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 62/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2258 - accuracy: 0.3933 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 63/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4217 - val_loss: 1.1809 - val_accuracy: 0.3652 Epoch 64/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3849 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 65/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3796 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 66/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2263 - accuracy: 0.3880 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 67/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.4069 - val_loss: 1.1849 - val_accuracy: 0.3652 Epoch 68/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3691 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 69/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3912 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 70/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3775 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 71/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3954 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 72/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4164 - val_loss: 1.1826 - val_accuracy: 0.3652 Epoch 73/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4259 - val_loss: 1.2038 - val_accuracy: 0.3652 Epoch 74/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2263 - accuracy: 0.4090 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 75/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2254 - accuracy: 0.4006 - val_loss: 1.1691 - val_accuracy: 0.4706 Epoch 76/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2252 - accuracy: 0.3817 - val_loss: 1.1733 - val_accuracy: 0.4706 Epoch 77/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.4059 - val_loss: 1.1886 - val_accuracy: 0.3652 Epoch 78/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3996 - val_loss: 1.1880 - val_accuracy: 0.3652 Epoch 79/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3901 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 80/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2245 - accuracy: 0.4154 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 81/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4111 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 82/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4090 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 83/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2261 - accuracy: 0.3933 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 84/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4322 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 85/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3985 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 86/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4059 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 87/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3796 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 88/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3796 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 89/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2260 - accuracy: 0.3891 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 90/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3964 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 91/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.4080 - val_loss: 1.1900 - val_accuracy: 0.3652 Epoch 92/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.4027 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 93/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3975 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 94/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 95/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4143 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 96/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4090 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 97/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3891 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 98/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4154 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 99/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2269 - accuracy: 0.4038 - val_loss: 1.1840 - val_accuracy: 0.3652 Epoch 100/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2248 - accuracy: 0.4143 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 101/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2259 - accuracy: 0.3933 - val_loss: 1.1851 - val_accuracy: 0.3652 Epoch 102/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3954 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 103/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4038 - val_loss: 1.1763 - val_accuracy: 0.3652 Epoch 104/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3922 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 105/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3985 - val_loss: 1.1748 - val_accuracy: 0.3652 Epoch 106/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4017 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 107/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4132 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 108/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3764 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 109/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3880 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 110/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3954 - val_loss: 1.1895 - val_accuracy: 0.3652 Epoch 111/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3996 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 112/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3838 - val_loss: 1.1737 - val_accuracy: 0.4706 Epoch 113/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3880 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 114/200 96/96 [==============================] - 0s 5ms/step - loss: 1.2262 - accuracy: 0.3933 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 115/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.4080 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 116/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3880 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 117/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4090 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 118/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4101 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 119/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3901 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 120/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3880 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 121/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3828 - val_loss: 1.1744 - val_accuracy: 0.4706 Epoch 122/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4017 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 123/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3922 - val_loss: 1.1826 - val_accuracy: 0.3652 Epoch 124/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3975 - val_loss: 1.1691 - val_accuracy: 0.4706 Epoch 125/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3891 - val_loss: 1.1813 - val_accuracy: 0.3652 Epoch 126/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4143 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 127/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3922 - val_loss: 1.1757 - val_accuracy: 0.4706 Epoch 128/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.4101 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 129/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4069 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 130/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4217 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 131/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4017 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 132/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3859 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 133/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4122 - val_loss: 1.1917 - val_accuracy: 0.3652 Epoch 134/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.4069 - val_loss: 1.1844 - val_accuracy: 0.3652 Epoch 135/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4132 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 136/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3954 - val_loss: 1.1849 - val_accuracy: 0.3652 Epoch 137/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3838 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 138/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4164 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 139/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3807 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 140/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4185 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 141/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3943 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 142/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1819 - val_accuracy: 0.3652 Epoch 143/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3838 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 144/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4048 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 145/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3933 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 146/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4027 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 147/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3807 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 148/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4080 - val_loss: 1.1772 - val_accuracy: 0.4706 Epoch 149/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.4090 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 150/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3922 - val_loss: 1.1741 - val_accuracy: 0.3652 Epoch 151/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4017 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 152/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4311 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 153/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3785 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 154/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3933 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 155/200 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3891 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 156/200 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4259 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 157/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.3964 - val_loss: 1.1906 - val_accuracy: 0.3652 Epoch 158/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4111 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 159/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3870 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 160/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4048 - val_loss: 1.1855 - val_accuracy: 0.3652 Epoch 161/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3870 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 162/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3785 - val_loss: 1.1897 - val_accuracy: 0.3652 Epoch 163/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.4111 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 164/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4132 - val_loss: 1.1961 - val_accuracy: 0.3652 Epoch 165/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3943 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 166/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3891 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 167/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3954 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 168/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3922 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 169/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3785 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 170/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4143 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 171/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3828 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 172/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2233 - accuracy: 0.4343 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 173/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3817 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 174/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2235 - accuracy: 0.4164 - val_loss: 1.1927 - val_accuracy: 0.3652 Epoch 175/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4122 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 176/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4111 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 177/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3817 - val_loss: 1.1755 - val_accuracy: 0.3652 Epoch 178/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4090 - val_loss: 1.1952 - val_accuracy: 0.3652 Epoch 179/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4059 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 180/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3912 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 181/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1775 - val_accuracy: 0.4706 Epoch 182/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3880 - val_loss: 1.1916 - val_accuracy: 0.3652 Epoch 183/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4090 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 184/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4017 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 185/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.4227 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 186/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4090 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 187/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4101 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 188/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4122 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 189/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3985 - val_loss: 1.1889 - val_accuracy: 0.3652 Epoch 190/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3933 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 191/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4069 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 192/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3975 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 193/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4048 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 194/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3870 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 195/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4027 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 196/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2266 - accuracy: 0.3754 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 197/200 96/96 [==============================] - 1s 6ms/step - loss: 1.2237 - accuracy: 0.4238 - val_loss: 1.1749 - val_accuracy: 0.3652 Epoch 198/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3680 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 199/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 200/200 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4185 - val_loss: 1.1901 - val_accuracy: 0.3652 13/13 [==============================] - 0s 3ms/step Epoch 1/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4006 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 2/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3828 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 3/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4143 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 4/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.3722 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 5/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2240 - accuracy: 0.4143 - val_loss: 1.1642 - val_accuracy: 0.4706 Epoch 6/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.3922 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 7/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2262 - accuracy: 0.3817 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 8/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2260 - accuracy: 0.3964 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 9/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 10/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.3964 - val_loss: 1.1757 - val_accuracy: 0.4706 Epoch 11/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.4006 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 12/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4280 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 13/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2242 - accuracy: 0.4154 - val_loss: 1.1928 - val_accuracy: 0.3652 Epoch 14/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.3901 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 15/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.3975 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 16/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.4038 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 17/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3912 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 18/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3964 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 19/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 20/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3828 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 21/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.4027 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 22/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3880 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 23/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3912 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 24/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3785 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 25/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3880 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 26/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3943 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 27/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4101 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 28/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 29/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3964 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 30/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3796 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 31/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3828 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 32/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4143 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 33/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.3912 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 34/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3870 - val_loss: 1.1758 - val_accuracy: 0.4706 Epoch 35/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4038 - val_loss: 1.1833 - val_accuracy: 0.3652 Epoch 36/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3880 - val_loss: 1.1903 - val_accuracy: 0.3652 Epoch 37/300 96/96 [==============================] - 0s 5ms/step - loss: 1.2257 - accuracy: 0.3996 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 38/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3912 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 39/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3712 - val_loss: 1.1826 - val_accuracy: 0.3652 Epoch 40/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3828 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 41/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1809 - val_accuracy: 0.3652 Epoch 42/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3985 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 43/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4080 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 44/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3933 - val_loss: 1.1871 - val_accuracy: 0.3652 Epoch 45/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 46/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3912 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 47/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4101 - val_loss: 1.1888 - val_accuracy: 0.3652 Epoch 48/300 96/96 [==============================] - 0s 5ms/step - loss: 1.2250 - accuracy: 0.4111 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 49/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3912 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 50/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3901 - val_loss: 1.1730 - val_accuracy: 0.4706 Epoch 51/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4280 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 52/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3922 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 53/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2260 - accuracy: 0.3912 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 54/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3817 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 55/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3807 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 56/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4101 - val_loss: 1.1960 - val_accuracy: 0.3652 Epoch 57/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3922 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 58/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.4090 - val_loss: 1.1811 - val_accuracy: 0.3652 Epoch 59/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3880 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 60/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4069 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 61/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3828 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 62/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4090 - val_loss: 1.1696 - val_accuracy: 0.4706 Epoch 63/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3891 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 64/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 65/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4027 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 66/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4227 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 67/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.3796 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 68/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.3975 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 69/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2230 - accuracy: 0.4301 - val_loss: 1.1870 - val_accuracy: 0.3652 Epoch 70/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3954 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 71/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3891 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 72/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3785 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 73/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3554 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 74/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3891 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 75/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4038 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 76/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.4017 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 77/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4006 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 78/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1936 - val_accuracy: 0.3652 Epoch 79/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2264 - accuracy: 0.3985 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 80/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3754 - val_loss: 1.1864 - val_accuracy: 0.3652 Epoch 81/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4248 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 82/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3996 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 83/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3943 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 84/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3922 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 85/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3891 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 86/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3785 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 87/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4111 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 88/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2230 - accuracy: 0.4290 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 89/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4069 - val_loss: 1.1837 - val_accuracy: 0.3652 Epoch 90/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4280 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 91/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3912 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 92/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3891 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 93/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4038 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 94/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3933 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 95/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3996 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 96/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3880 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 97/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2266 - accuracy: 0.3859 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 98/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4090 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 99/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3828 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 100/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3712 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 101/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3796 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 102/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4122 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 103/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3954 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 104/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3785 - val_loss: 1.1819 - val_accuracy: 0.3652 Epoch 105/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3943 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 106/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3922 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 107/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3954 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 108/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4006 - val_loss: 1.1911 - val_accuracy: 0.3652 Epoch 109/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2244 - accuracy: 0.4154 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 110/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4027 - val_loss: 1.1748 - val_accuracy: 0.3652 Epoch 111/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4059 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 112/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3880 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 113/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3849 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 114/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3922 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 115/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3943 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 116/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 117/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3722 - val_loss: 1.1819 - val_accuracy: 0.3652 Epoch 118/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3870 - val_loss: 1.1891 - val_accuracy: 0.3652 Epoch 119/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3954 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 120/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4059 - val_loss: 1.1921 - val_accuracy: 0.3652 Epoch 121/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3964 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 122/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2235 - accuracy: 0.4048 - val_loss: 1.1790 - val_accuracy: 0.3652 Epoch 123/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4038 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 124/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4111 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 125/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3880 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 126/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3649 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 127/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4122 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 128/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4175 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 129/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3796 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 130/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4006 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 131/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3985 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 132/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4006 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 133/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.4069 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 134/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3933 - val_loss: 1.1739 - val_accuracy: 0.4706 Epoch 135/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.4006 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 136/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.3975 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 137/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.3817 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 138/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.4196 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 139/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4227 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 140/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3785 - val_loss: 1.1746 - val_accuracy: 0.4706 Epoch 141/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.4038 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 142/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3554 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 143/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4101 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 144/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3933 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 145/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4059 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 146/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.3933 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 147/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4038 - val_loss: 1.1906 - val_accuracy: 0.3652 Epoch 148/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4048 - val_loss: 1.1744 - val_accuracy: 0.3652 Epoch 149/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3764 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 150/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3996 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 151/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3964 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 152/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3775 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 153/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3891 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 154/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3922 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 155/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3933 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 156/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2232 - accuracy: 0.4206 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 157/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4038 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 158/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3975 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 159/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2271 - accuracy: 0.3775 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 160/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3954 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 161/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3817 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 162/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4027 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 163/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3964 - val_loss: 1.1740 - val_accuracy: 0.4706 Epoch 164/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3880 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 165/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 166/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3964 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 167/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.3901 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 168/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3880 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 169/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3880 - val_loss: 1.1747 - val_accuracy: 0.4706 Epoch 170/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.4259 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 171/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4101 - val_loss: 1.1750 - val_accuracy: 0.4706 Epoch 172/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3933 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 173/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1742 - val_accuracy: 0.3652 Epoch 174/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2241 - accuracy: 0.4069 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 175/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3880 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 176/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3733 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 177/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3743 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 178/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 179/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 180/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3817 - val_loss: 1.1811 - val_accuracy: 0.3652 Epoch 181/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4248 - val_loss: 1.1865 - val_accuracy: 0.3652 Epoch 182/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4269 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 183/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3985 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 184/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3438 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 185/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3996 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 186/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2240 - accuracy: 0.4122 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 187/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4101 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 188/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3912 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 189/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4048 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 190/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.3701 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 191/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3912 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 192/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2194 - accuracy: 0.4322 - val_loss: 1.2181 - val_accuracy: 0.3652 Epoch 193/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4101 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 194/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4143 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 195/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3922 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 196/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3817 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 197/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3859 - val_loss: 1.1777 - val_accuracy: 0.3652 Epoch 198/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4027 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 199/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4059 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 200/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3985 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 201/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4111 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 202/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4006 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 203/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3891 - val_loss: 1.1780 - val_accuracy: 0.3652 Epoch 204/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3891 - val_loss: 1.1818 - val_accuracy: 0.3652 Epoch 205/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3796 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 206/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3912 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 207/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3933 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 208/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4185 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 209/300 96/96 [==============================] - 0s 5ms/step - loss: 1.2243 - accuracy: 0.4164 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 210/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3975 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 211/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3922 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 212/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4248 - val_loss: 1.1855 - val_accuracy: 0.3652 Epoch 213/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3775 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 214/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4111 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 215/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4122 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 216/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3754 - val_loss: 1.1833 - val_accuracy: 0.3652 Epoch 217/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3912 - val_loss: 1.1889 - val_accuracy: 0.3652 Epoch 218/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.4006 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 219/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3943 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 220/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3954 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 221/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2238 - accuracy: 0.4175 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 222/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2235 - accuracy: 0.4111 - val_loss: 1.1953 - val_accuracy: 0.3652 Epoch 223/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2268 - accuracy: 0.3975 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 224/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.3943 - val_loss: 1.1648 - val_accuracy: 0.4706 Epoch 225/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.4111 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 226/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2238 - accuracy: 0.4206 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 227/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4059 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 228/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2234 - accuracy: 0.4238 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 229/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3985 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 230/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2256 - accuracy: 0.3817 - val_loss: 1.1829 - val_accuracy: 0.3652 Epoch 231/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.3838 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 232/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3943 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 233/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3954 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 234/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3912 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 235/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3807 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 236/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3785 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 237/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4038 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 238/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3922 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 239/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2268 - accuracy: 0.3849 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 240/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4122 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 241/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4017 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 242/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3891 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 243/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4006 - val_loss: 1.1777 - val_accuracy: 0.3652 Epoch 244/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4027 - val_loss: 1.1946 - val_accuracy: 0.3652 Epoch 245/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3722 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 246/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.4196 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 247/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3880 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 248/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3933 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 249/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4196 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 250/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3870 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 251/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3901 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 252/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3975 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 253/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1737 - val_accuracy: 0.4706 Epoch 254/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4111 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 255/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4038 - val_loss: 1.1931 - val_accuracy: 0.3652 Epoch 256/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4069 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 257/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4069 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 258/300 96/96 [==============================] - 1s 9ms/step - loss: 1.2256 - accuracy: 0.3796 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 259/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4280 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 260/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.3796 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 261/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2244 - accuracy: 0.4090 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 262/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 263/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2240 - accuracy: 0.4090 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 264/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.3754 - val_loss: 1.1763 - val_accuracy: 0.3652 Epoch 265/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.4080 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 266/300 96/96 [==============================] - 1s 8ms/step - loss: 1.2255 - accuracy: 0.3912 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 267/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4038 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 268/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3964 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 269/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4017 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 270/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3870 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 271/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4185 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 272/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4038 - val_loss: 1.1890 - val_accuracy: 0.3652 Epoch 273/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 274/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4111 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 275/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4006 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 276/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4027 - val_loss: 1.1811 - val_accuracy: 0.3652 Epoch 277/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4048 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 278/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3985 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 279/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3954 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 280/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4143 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 281/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3975 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 282/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3901 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 283/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3954 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 284/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 285/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3964 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 286/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3838 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 287/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3891 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 288/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.4048 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 289/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3649 - val_loss: 1.1872 - val_accuracy: 0.3652 Epoch 290/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.4027 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 291/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3849 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 292/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3859 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 293/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2240 - accuracy: 0.4132 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 294/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4006 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 295/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3880 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 296/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3807 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 297/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3891 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 298/300 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4006 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 299/300 96/96 [==============================] - 1s 5ms/step - loss: 1.2235 - accuracy: 0.4069 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 300/300 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3985 - val_loss: 1.1802 - val_accuracy: 0.3652 13/13 [==============================] - 0s 3ms/step Epoch 1/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.4027 - val_loss: 1.1888 - val_accuracy: 0.3652 Epoch 2/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.4101 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 3/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3870 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 4/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3670 - val_loss: 1.1696 - val_accuracy: 0.4706 Epoch 5/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4090 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 6/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.4196 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 7/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3954 - val_loss: 1.1780 - val_accuracy: 0.3652 Epoch 8/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.4017 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 9/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3680 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 10/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3954 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 11/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4059 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 12/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3870 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 13/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.4017 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 14/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3943 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 15/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.3817 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 16/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 17/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4048 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 18/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4111 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 19/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3901 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 20/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4017 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 21/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4217 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 22/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4069 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 23/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3880 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 24/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3796 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 25/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4027 - val_loss: 1.1759 - val_accuracy: 0.4706 Epoch 26/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 27/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.4017 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 28/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3943 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 29/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.3954 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 30/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3943 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 31/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4101 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 32/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4090 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 33/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3964 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 34/400 96/96 [==============================] - 1s 9ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1884 - val_accuracy: 0.3652 Epoch 35/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3828 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 36/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.3785 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 37/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4290 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 38/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4132 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 39/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 40/400 96/96 [==============================] - 0s 5ms/step - loss: 1.2258 - accuracy: 0.3807 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 41/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4154 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 42/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3712 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 43/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.4122 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 44/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3933 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 45/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4069 - val_loss: 1.1801 - val_accuracy: 0.3652 Epoch 46/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1742 - val_accuracy: 0.3652 Epoch 47/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3554 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 48/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4027 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 49/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3943 - val_loss: 1.1759 - val_accuracy: 0.4706 Epoch 50/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3975 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 51/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4206 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 52/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3912 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 53/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3975 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 54/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4090 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 55/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3754 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 56/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3807 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 57/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3701 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 58/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3880 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 59/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4006 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 60/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3838 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 61/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3870 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 62/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3922 - val_loss: 1.1740 - val_accuracy: 0.4706 Epoch 63/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3943 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 64/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3796 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 65/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4175 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 66/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3838 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 67/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4038 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 68/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4111 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 69/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4069 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 70/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3996 - val_loss: 1.1880 - val_accuracy: 0.3652 Epoch 71/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.3964 - val_loss: 1.1734 - val_accuracy: 0.4706 Epoch 72/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3891 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 73/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4027 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 74/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.4017 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 75/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3838 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 76/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3891 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 77/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3743 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 78/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4080 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 79/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3870 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 80/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4132 - val_loss: 1.1754 - val_accuracy: 0.4706 Epoch 81/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3975 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 82/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4006 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 83/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4048 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 84/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3891 - val_loss: 1.1888 - val_accuracy: 0.3652 Epoch 85/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4017 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 86/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3964 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 87/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3996 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 88/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3901 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 89/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3849 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 90/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4069 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 91/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3828 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 92/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3764 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 93/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1861 - val_accuracy: 0.3652 Epoch 94/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3891 - val_loss: 1.1854 - val_accuracy: 0.3652 Epoch 95/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3712 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 96/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2237 - accuracy: 0.4069 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 97/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.4038 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 98/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4006 - val_loss: 1.1878 - val_accuracy: 0.3652 Epoch 99/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3870 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 100/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3849 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 101/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.3975 - val_loss: 1.1728 - val_accuracy: 0.4706 Epoch 102/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4069 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 103/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3996 - val_loss: 1.1868 - val_accuracy: 0.3652 Epoch 104/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4069 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 105/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3838 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 106/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 107/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 108/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3943 - val_loss: 1.1799 - val_accuracy: 0.3652 Epoch 109/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.3922 - val_loss: 1.1948 - val_accuracy: 0.3652 Epoch 110/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4154 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 111/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3891 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 112/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3796 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 113/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 114/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3964 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 115/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3838 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 116/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4048 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 117/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4006 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 118/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4027 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 119/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3722 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 120/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3785 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 121/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3943 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 122/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3922 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 123/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3933 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 124/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.3922 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 125/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3828 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 126/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4122 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 127/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4101 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 128/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4090 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 129/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 130/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3922 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 131/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2230 - accuracy: 0.4269 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 132/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2227 - accuracy: 0.4196 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 133/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.3996 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 134/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2245 - accuracy: 0.3901 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 135/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.3891 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 136/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2255 - accuracy: 0.3975 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 137/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2238 - accuracy: 0.3975 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 138/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4080 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 139/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4196 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 140/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3880 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 141/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4090 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 142/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.3891 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 143/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3943 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 144/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3901 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 145/400 96/96 [==============================] - 0s 5ms/step - loss: 1.2252 - accuracy: 0.3912 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 146/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4154 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 147/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3817 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 148/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3859 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 149/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3870 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 150/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4027 - val_loss: 1.1716 - val_accuracy: 0.3652 Epoch 151/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3849 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 152/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3880 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 153/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2244 - accuracy: 0.4080 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 154/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3891 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 155/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4101 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 156/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.3943 - val_loss: 1.1748 - val_accuracy: 0.3652 Epoch 157/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4080 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 158/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4006 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 159/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 160/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3859 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 161/400 96/96 [==============================] - 0s 5ms/step - loss: 1.2241 - accuracy: 0.4206 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 162/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4122 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 163/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3964 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 164/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4090 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 165/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.4006 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 166/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.4090 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 167/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4048 - val_loss: 1.1911 - val_accuracy: 0.3652 Epoch 168/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3754 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 169/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.3975 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 170/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3954 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 171/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2232 - accuracy: 0.3964 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 172/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3754 - val_loss: 1.1874 - val_accuracy: 0.3652 Epoch 173/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3901 - val_loss: 1.1823 - val_accuracy: 0.3652 Epoch 174/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3954 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 175/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2231 - accuracy: 0.4122 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 176/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4154 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 177/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3796 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 178/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4059 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 179/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3985 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 180/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3880 - val_loss: 1.1809 - val_accuracy: 0.3652 Epoch 181/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3785 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 182/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3880 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 183/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3912 - val_loss: 1.1747 - val_accuracy: 0.4706 Epoch 184/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 185/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3954 - val_loss: 1.1871 - val_accuracy: 0.3652 Epoch 186/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3880 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 187/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.3912 - val_loss: 1.1799 - val_accuracy: 0.3652 Epoch 188/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3922 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 189/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3975 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 190/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4006 - val_loss: 1.1761 - val_accuracy: 0.4706 Epoch 191/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3964 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 192/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4122 - val_loss: 1.1984 - val_accuracy: 0.3652 Epoch 193/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.3859 - val_loss: 1.1903 - val_accuracy: 0.3652 Epoch 194/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3922 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 195/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 196/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3817 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 197/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3996 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 198/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2239 - accuracy: 0.4101 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 199/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.3975 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 200/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 201/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4069 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 202/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3901 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 203/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4269 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 204/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3849 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 205/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3975 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 206/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3954 - val_loss: 1.1745 - val_accuracy: 0.4706 Epoch 207/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2239 - accuracy: 0.4217 - val_loss: 1.1944 - val_accuracy: 0.3652 Epoch 208/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2260 - accuracy: 0.3975 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 209/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3754 - val_loss: 1.1849 - val_accuracy: 0.3652 Epoch 210/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4143 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 211/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3870 - val_loss: 1.1738 - val_accuracy: 0.4706 Epoch 212/400 96/96 [==============================] - 0s 5ms/step - loss: 1.2253 - accuracy: 0.3912 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 213/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4048 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 214/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3922 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 215/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4069 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 216/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3743 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 217/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4017 - val_loss: 1.1864 - val_accuracy: 0.3652 Epoch 218/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4280 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 219/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4175 - val_loss: 1.2040 - val_accuracy: 0.3652 Epoch 220/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4248 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 221/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3922 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 222/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3712 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 223/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3922 - val_loss: 1.1731 - val_accuracy: 0.4706 Epoch 224/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1746 - val_accuracy: 0.3652 Epoch 225/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.3943 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 226/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4038 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 227/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 228/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3828 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 229/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3922 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 230/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4069 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 231/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3870 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 232/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3775 - val_loss: 1.1777 - val_accuracy: 0.3652 Epoch 233/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3996 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 234/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4080 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 235/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 236/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4343 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 237/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4017 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 238/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4248 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 239/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.3764 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 240/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4101 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 241/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2244 - accuracy: 0.4017 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 242/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.3880 - val_loss: 1.1780 - val_accuracy: 0.3652 Epoch 243/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.3922 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 244/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.4017 - val_loss: 1.1752 - val_accuracy: 0.4706 Epoch 245/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2249 - accuracy: 0.4122 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 246/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.3785 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 247/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 248/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4059 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 249/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4027 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 250/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2228 - accuracy: 0.4290 - val_loss: 1.1891 - val_accuracy: 0.3652 Epoch 251/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.4006 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 252/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3975 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 253/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3922 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 254/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3943 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 255/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 256/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.4090 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 257/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.4017 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 258/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 259/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3985 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 260/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4017 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 261/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3838 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 262/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3964 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 263/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.3943 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 264/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2242 - accuracy: 0.3838 - val_loss: 1.1867 - val_accuracy: 0.3652 Epoch 265/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.4038 - val_loss: 1.1885 - val_accuracy: 0.3652 Epoch 266/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3838 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 267/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4069 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 268/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4017 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 269/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3933 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 270/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4227 - val_loss: 1.1743 - val_accuracy: 0.3652 Epoch 271/400 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.4069 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 272/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3796 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 273/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3796 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 274/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4080 - val_loss: 1.1886 - val_accuracy: 0.3652 Epoch 275/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3964 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 276/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4090 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 277/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3880 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 278/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4185 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 279/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3859 - val_loss: 1.1955 - val_accuracy: 0.3652 Epoch 280/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2269 - accuracy: 0.4069 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 281/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4122 - val_loss: 1.1959 - val_accuracy: 0.3652 Epoch 282/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4196 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 283/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3901 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 284/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4111 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 285/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2234 - accuracy: 0.4290 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 286/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.3954 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 287/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3754 - val_loss: 1.1799 - val_accuracy: 0.3652 Epoch 288/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3807 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 289/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3901 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 290/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4017 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 291/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3817 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 292/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3996 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 293/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3912 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 294/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4248 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 295/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4196 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 296/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4006 - val_loss: 1.1888 - val_accuracy: 0.3652 Epoch 297/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.4017 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 298/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4185 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 299/400 96/96 [==============================] - 1s 5ms/step - 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loss: 1.2250 - accuracy: 0.3859 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 307/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3796 - val_loss: 1.1746 - val_accuracy: 0.3652 Epoch 308/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4006 - val_loss: 1.1734 - val_accuracy: 0.4706 Epoch 309/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3996 - val_loss: 1.1872 - val_accuracy: 0.3652 Epoch 310/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4017 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 311/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3849 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 312/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.4038 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 313/400 96/96 [==============================] - 1s 5ms/step - 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loss: 1.2251 - accuracy: 0.3891 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 321/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3743 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 322/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.3849 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 323/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3901 - val_loss: 1.1747 - val_accuracy: 0.4706 Epoch 324/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2235 - accuracy: 0.4111 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 325/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4080 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 326/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3743 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 327/400 96/96 [==============================] - 1s 5ms/step - 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loss: 1.2255 - accuracy: 0.3933 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 342/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4111 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 343/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2236 - accuracy: 0.4175 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 344/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3764 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 345/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3891 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 346/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4206 - val_loss: 1.1921 - val_accuracy: 0.3652 Epoch 347/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4069 - val_loss: 1.1886 - val_accuracy: 0.3652 Epoch 348/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3870 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 349/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3849 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 350/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3870 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 351/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3964 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 352/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 353/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3912 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 354/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3502 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 355/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3933 - val_loss: 1.1859 - val_accuracy: 0.3652 Epoch 356/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4206 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 357/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4080 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 358/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4196 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 359/400 96/96 [==============================] - 0s 5ms/step - loss: 1.2262 - accuracy: 0.3870 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 360/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4175 - val_loss: 1.1865 - val_accuracy: 0.3652 Epoch 361/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4048 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 362/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4122 - val_loss: 1.1870 - val_accuracy: 0.3652 Epoch 363/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4227 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 364/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3912 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 365/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3743 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 366/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3975 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 367/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3764 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 368/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3943 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 369/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.3996 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 370/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.3807 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 371/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 372/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.4111 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 373/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3985 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 374/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3870 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 375/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.4227 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 376/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4017 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 377/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3796 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 378/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3828 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 379/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3975 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 380/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3859 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 381/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4006 - val_loss: 1.1865 - val_accuracy: 0.3652 Epoch 382/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.4048 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 383/400 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.3964 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 384/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4059 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 385/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3943 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 386/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4080 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 387/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3838 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 388/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3985 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 389/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3954 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 390/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3701 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 391/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3964 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 392/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3838 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 393/400 96/96 [==============================] - 1s 6ms/step - loss: 1.2232 - accuracy: 0.4290 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 394/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3912 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 395/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4048 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 396/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2237 - accuracy: 0.4301 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 397/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3975 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 398/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3828 - val_loss: 1.1739 - val_accuracy: 0.4706 Epoch 399/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3922 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 400/400 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4101 - val_loss: 1.1665 - val_accuracy: 0.4706 13/13 [==============================] - 0s 3ms/step Epoch 1/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 2/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 3/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4259 - val_loss: 1.1901 - val_accuracy: 0.3652 Epoch 4/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.4143 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 5/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.3954 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 6/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.3996 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 7/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3943 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 8/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3933 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 9/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3775 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 10/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.4027 - val_loss: 1.1732 - val_accuracy: 0.4706 Epoch 11/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2262 - accuracy: 0.3901 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 12/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3638 - val_loss: 1.1741 - val_accuracy: 0.3652 Epoch 13/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2257 - accuracy: 0.3901 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 14/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3796 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 15/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3859 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 16/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3901 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 17/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3922 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 18/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3870 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 19/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3817 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 20/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1743 - val_accuracy: 0.4706 Epoch 21/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3680 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 22/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3817 - val_loss: 1.1811 - val_accuracy: 0.3652 Epoch 23/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4017 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 24/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 25/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3849 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 26/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3828 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 27/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2238 - accuracy: 0.4111 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 28/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2241 - accuracy: 0.4059 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 29/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 30/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4048 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 31/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2236 - accuracy: 0.4122 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 32/500 96/96 [==============================] - 0s 5ms/step - loss: 1.2250 - accuracy: 0.3796 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 33/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3922 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 34/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4122 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 35/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.4069 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 36/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4238 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 37/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.3975 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 38/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2238 - accuracy: 0.4027 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 39/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.3933 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 40/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.4080 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 41/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.4122 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 42/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.3838 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 43/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4006 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 44/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.3901 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 45/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3964 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 46/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4090 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 47/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 48/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3922 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 49/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.3985 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 50/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.4038 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 51/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.3912 - val_loss: 1.1859 - val_accuracy: 0.3652 Epoch 52/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3859 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 53/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4027 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 54/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2235 - accuracy: 0.4280 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 55/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4143 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 56/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3922 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 57/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3859 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 58/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3817 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 59/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3985 - val_loss: 1.1790 - val_accuracy: 0.3652 Epoch 60/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2237 - accuracy: 0.4269 - val_loss: 1.1920 - val_accuracy: 0.3652 Epoch 61/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2261 - accuracy: 0.3996 - val_loss: 1.1844 - val_accuracy: 0.3652 Epoch 62/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4017 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 63/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3796 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 64/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2241 - accuracy: 0.4143 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 65/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1871 - val_accuracy: 0.3652 Epoch 66/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4185 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 67/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 68/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4027 - val_loss: 1.1763 - val_accuracy: 0.3652 Epoch 69/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 70/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3796 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 71/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3891 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 72/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4017 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 73/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4111 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 74/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4048 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 75/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2216 - accuracy: 0.4269 - val_loss: 1.2009 - val_accuracy: 0.3652 Epoch 76/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4059 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 77/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4090 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 78/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2218 - accuracy: 0.4343 - val_loss: 1.1881 - val_accuracy: 0.3652 Epoch 79/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4090 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 80/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3985 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 81/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4006 - val_loss: 1.1763 - val_accuracy: 0.3652 Epoch 82/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3996 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 83/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3764 - val_loss: 1.1789 - val_accuracy: 0.3652 Epoch 84/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3964 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 85/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4048 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 86/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4069 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 87/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4090 - val_loss: 1.1841 - val_accuracy: 0.3652 Epoch 88/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 89/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4248 - val_loss: 1.1886 - val_accuracy: 0.3652 Epoch 90/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3901 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 91/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3859 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 92/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3954 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 93/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2227 - accuracy: 0.4248 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 94/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3975 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 95/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4059 - val_loss: 1.1854 - val_accuracy: 0.3652 Epoch 96/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3891 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 97/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3922 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 98/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4080 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 99/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4017 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 100/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4090 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 101/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4132 - val_loss: 1.1840 - val_accuracy: 0.3652 Epoch 102/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3985 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 103/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.4017 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 104/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3807 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 105/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4185 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 106/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4059 - val_loss: 1.1879 - val_accuracy: 0.3652 Epoch 107/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3964 - val_loss: 1.1736 - val_accuracy: 0.3652 Epoch 108/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3817 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 109/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 110/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2236 - accuracy: 0.4143 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 111/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4038 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 112/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2236 - accuracy: 0.4101 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 113/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4080 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 114/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3754 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 115/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2231 - accuracy: 0.4206 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 116/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2241 - accuracy: 0.4038 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 117/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 118/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3733 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 119/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3922 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 120/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4090 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 121/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3838 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 122/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4069 - val_loss: 1.1829 - val_accuracy: 0.3652 Epoch 123/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.4227 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 124/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3933 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 125/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2239 - accuracy: 0.4238 - val_loss: 1.1863 - val_accuracy: 0.3652 Epoch 126/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 127/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 128/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4048 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 129/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3922 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 130/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3922 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 131/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3796 - val_loss: 1.1872 - val_accuracy: 0.3652 Epoch 132/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4090 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 133/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4185 - val_loss: 1.1765 - val_accuracy: 0.4706 Epoch 134/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.4038 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 135/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4080 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 136/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3828 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 137/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3701 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 138/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3922 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 139/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 140/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3985 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 141/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2231 - accuracy: 0.4185 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 142/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.3964 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 143/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2245 - accuracy: 0.4196 - val_loss: 1.1836 - val_accuracy: 0.3652 Epoch 144/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4080 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 145/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.3722 - val_loss: 1.1763 - val_accuracy: 0.3652 Epoch 146/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2249 - accuracy: 0.4122 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 147/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4101 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 148/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2249 - accuracy: 0.3985 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 149/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2238 - accuracy: 0.4164 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 150/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 151/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3859 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 152/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4132 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 153/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3943 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 154/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4185 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 155/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.4017 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 156/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 157/500 96/96 [==============================] - 0s 5ms/step - loss: 1.2249 - accuracy: 0.4101 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 158/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4048 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 159/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.4164 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 160/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3912 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 161/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3922 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 162/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2233 - accuracy: 0.4227 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 163/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.3933 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 164/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4090 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 165/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3849 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 166/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4017 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 167/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.3796 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 168/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3975 - val_loss: 1.1882 - val_accuracy: 0.3652 Epoch 169/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4248 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 170/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.3912 - val_loss: 1.1830 - val_accuracy: 0.3652 Epoch 171/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3722 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 172/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3775 - val_loss: 1.1738 - val_accuracy: 0.4706 Epoch 173/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3775 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 174/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3764 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 175/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 176/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3796 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 177/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3691 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 178/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.3796 - val_loss: 1.1752 - val_accuracy: 0.4706 Epoch 179/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3943 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 180/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.3933 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 181/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3912 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 182/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4017 - val_loss: 1.1746 - val_accuracy: 0.3652 Epoch 183/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4027 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 184/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3891 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 185/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4185 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 186/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4217 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 187/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3838 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 188/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3807 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 189/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4048 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 190/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.3975 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 191/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3922 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 192/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4017 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 193/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3743 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 194/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3891 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 195/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 196/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4143 - val_loss: 1.1884 - val_accuracy: 0.3652 Epoch 197/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4080 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 198/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3912 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 199/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3943 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 200/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3828 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 201/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4059 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 202/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2244 - accuracy: 0.3954 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 203/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.3912 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 204/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3964 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 205/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3954 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 206/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4048 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 207/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3933 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 208/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3838 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 209/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3617 - val_loss: 1.1789 - val_accuracy: 0.3652 Epoch 210/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4090 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 211/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.4038 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 212/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4164 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 213/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3828 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 214/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3859 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 215/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4038 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 216/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3943 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 217/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4069 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 218/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4122 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 219/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.3964 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 220/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3849 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 221/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3996 - val_loss: 1.1748 - val_accuracy: 0.4706 Epoch 222/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3785 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 223/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4069 - val_loss: 1.1830 - val_accuracy: 0.3652 Epoch 224/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4006 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 225/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2240 - accuracy: 0.4059 - val_loss: 1.1931 - val_accuracy: 0.3652 Epoch 226/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3933 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 227/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4048 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 228/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3859 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 229/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4164 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 230/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4038 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 231/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3954 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 232/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3838 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 233/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3912 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 234/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3743 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 235/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3828 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 236/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3859 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 237/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3891 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 238/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4017 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 239/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3796 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 240/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2236 - accuracy: 0.4048 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 241/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4059 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 242/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.3880 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 243/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4069 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 244/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3922 - val_loss: 1.1742 - val_accuracy: 0.3652 Epoch 245/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4122 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 246/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2239 - accuracy: 0.4080 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 247/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2256 - accuracy: 0.3754 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 248/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2232 - accuracy: 0.4059 - val_loss: 1.1762 - val_accuracy: 0.4706 Epoch 249/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2249 - accuracy: 0.4132 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 250/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2231 - accuracy: 0.4143 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 251/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2244 - accuracy: 0.3933 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 252/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.3954 - val_loss: 1.1779 - val_accuracy: 0.3652 Epoch 253/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.3933 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 254/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 255/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2240 - accuracy: 0.3996 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 256/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3912 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 257/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4006 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 258/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.3964 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 259/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3607 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 260/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.3996 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 261/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.3870 - val_loss: 1.1843 - val_accuracy: 0.3652 Epoch 262/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4090 - val_loss: 1.1959 - val_accuracy: 0.3652 Epoch 263/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.3880 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 264/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3722 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 265/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2255 - accuracy: 0.3870 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 266/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.4006 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 267/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2227 - accuracy: 0.4132 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 268/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3954 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 269/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3891 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 270/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3512 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 271/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4038 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 272/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4027 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 273/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3922 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 274/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.3785 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 275/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3975 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 276/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3870 - val_loss: 1.1740 - val_accuracy: 0.4706 Epoch 277/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3975 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 278/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3964 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 279/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2232 - accuracy: 0.4164 - val_loss: 1.1926 - val_accuracy: 0.3652 Epoch 280/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.3817 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 281/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4080 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 282/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3870 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 283/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3828 - val_loss: 1.1741 - val_accuracy: 0.3652 Epoch 284/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2238 - accuracy: 0.4132 - val_loss: 1.1906 - val_accuracy: 0.3652 Epoch 285/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4090 - val_loss: 1.1755 - val_accuracy: 0.3652 Epoch 286/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4059 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 287/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3933 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 288/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4059 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 289/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3817 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 290/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4069 - val_loss: 1.1829 - val_accuracy: 0.3652 Epoch 291/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3985 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 292/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3891 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 293/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3901 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 294/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2240 - accuracy: 0.4196 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 295/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3701 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 296/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2209 - accuracy: 0.4395 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 297/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4132 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 298/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.4006 - val_loss: 1.1855 - val_accuracy: 0.3652 Epoch 299/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.3743 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 300/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.4017 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 301/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.4048 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 302/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.3807 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 303/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2255 - accuracy: 0.3964 - val_loss: 1.1738 - val_accuracy: 0.4706 Epoch 304/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.3943 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 305/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4048 - val_loss: 1.1755 - val_accuracy: 0.3652 Epoch 306/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3922 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 307/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3975 - val_loss: 1.1729 - val_accuracy: 0.4706 Epoch 308/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3912 - val_loss: 1.1736 - val_accuracy: 0.3652 Epoch 309/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4048 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 310/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3996 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 311/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 312/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3859 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 313/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2233 - accuracy: 0.4090 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 314/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3764 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 315/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4101 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 316/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3933 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 317/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3964 - val_loss: 1.1829 - val_accuracy: 0.3652 Epoch 318/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 319/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 320/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2236 - accuracy: 0.4154 - val_loss: 1.1909 - val_accuracy: 0.3652 Epoch 321/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4175 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 322/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4027 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 323/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3849 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 324/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4027 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 325/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3996 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 326/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4069 - val_loss: 1.1739 - val_accuracy: 0.4706 Epoch 327/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.3733 - val_loss: 1.1745 - val_accuracy: 0.4706 Epoch 328/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4101 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 329/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2236 - accuracy: 0.4259 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 330/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2214 - accuracy: 0.4364 - val_loss: 1.2030 - val_accuracy: 0.3652 Epoch 331/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2262 - accuracy: 0.4090 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 332/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3964 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 333/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3964 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 334/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2244 - accuracy: 0.4154 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 335/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3891 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 336/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4069 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 337/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3891 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 338/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3838 - val_loss: 1.1875 - val_accuracy: 0.3652 Epoch 339/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4175 - val_loss: 1.1737 - val_accuracy: 0.4706 Epoch 340/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3975 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 341/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3880 - val_loss: 1.1734 - val_accuracy: 0.4706 Epoch 342/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3954 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 343/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2240 - accuracy: 0.4111 - val_loss: 1.1738 - val_accuracy: 0.4706 Epoch 344/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4048 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 345/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3880 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 346/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3859 - val_loss: 1.1747 - val_accuracy: 0.3652 Epoch 347/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3838 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 348/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2232 - accuracy: 0.4185 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 349/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4006 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 350/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3985 - val_loss: 1.1729 - val_accuracy: 0.4706 Epoch 351/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3754 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 352/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3975 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 353/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2237 - accuracy: 0.4280 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 354/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4090 - val_loss: 1.1941 - val_accuracy: 0.3652 Epoch 355/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3817 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 356/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.3796 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 357/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3985 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 358/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3838 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 359/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2233 - accuracy: 0.4080 - val_loss: 1.1947 - val_accuracy: 0.3652 Epoch 360/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.3964 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 361/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.3954 - val_loss: 1.1851 - val_accuracy: 0.3652 Epoch 362/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2257 - accuracy: 0.4059 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 363/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3712 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 364/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3985 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 365/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3975 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 366/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.3817 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 367/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4027 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 368/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4080 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 369/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4027 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 370/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3764 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 371/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4164 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 372/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3975 - val_loss: 1.1819 - val_accuracy: 0.3652 Epoch 373/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4143 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 374/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2256 - accuracy: 0.4038 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 375/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3743 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 376/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4154 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 377/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4017 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 378/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.3943 - val_loss: 1.1938 - val_accuracy: 0.3652 Epoch 379/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.4059 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 380/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3891 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 381/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3733 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 382/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.3785 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 383/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4090 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 384/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4059 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 385/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4038 - val_loss: 1.1746 - val_accuracy: 0.3652 Epoch 386/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3891 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 387/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4006 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 388/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2245 - accuracy: 0.4090 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 389/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.3754 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 390/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.3870 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 391/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.3638 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 392/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4027 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 393/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3975 - val_loss: 1.1777 - val_accuracy: 0.3652 Epoch 394/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2242 - accuracy: 0.4111 - val_loss: 1.1858 - val_accuracy: 0.3652 Epoch 395/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.3859 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 396/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3828 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 397/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3870 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 398/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2214 - accuracy: 0.4343 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 399/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2221 - accuracy: 0.4353 - val_loss: 1.2003 - val_accuracy: 0.3652 Epoch 400/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2275 - accuracy: 0.3849 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 401/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2233 - accuracy: 0.4238 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 402/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.3985 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 403/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2243 - accuracy: 0.4090 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 404/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.3764 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 405/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.4038 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 406/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.3964 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 407/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.4185 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 408/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2239 - accuracy: 0.4101 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 409/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2238 - accuracy: 0.4122 - val_loss: 1.1868 - val_accuracy: 0.3652 Epoch 410/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.4027 - val_loss: 1.1777 - val_accuracy: 0.4706 Epoch 411/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3954 - val_loss: 1.1841 - val_accuracy: 0.3652 Epoch 412/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4017 - val_loss: 1.1900 - val_accuracy: 0.3652 Epoch 413/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4059 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 414/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4027 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 415/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2250 - accuracy: 0.3933 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 416/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.3859 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 417/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4027 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 418/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4017 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 419/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.3964 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 420/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.3996 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 421/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.3964 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 422/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3996 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 423/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4027 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 424/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3912 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 425/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4101 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 426/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2243 - accuracy: 0.4017 - val_loss: 1.1876 - val_accuracy: 0.3652 Epoch 427/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2236 - accuracy: 0.4311 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 428/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3712 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 429/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.3849 - val_loss: 1.1754 - val_accuracy: 0.4706 Epoch 430/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2249 - accuracy: 0.4206 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 431/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2254 - accuracy: 0.3712 - val_loss: 1.1813 - val_accuracy: 0.3652 Epoch 432/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.3880 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 433/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2244 - accuracy: 0.4017 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 434/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2241 - accuracy: 0.4185 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 435/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4027 - val_loss: 1.1691 - val_accuracy: 0.4706 Epoch 436/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3870 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 437/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2236 - accuracy: 0.4090 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 438/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2264 - accuracy: 0.3964 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 439/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3954 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 440/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4027 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 441/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3996 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 442/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3996 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 443/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3807 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 444/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.3891 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 445/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2242 - accuracy: 0.4059 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 446/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2237 - accuracy: 0.4238 - val_loss: 1.1891 - val_accuracy: 0.3652 Epoch 447/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3943 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 448/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3870 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 449/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3817 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 450/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2246 - accuracy: 0.4080 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 451/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 452/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2258 - accuracy: 0.3796 - val_loss: 1.1740 - val_accuracy: 0.4706 Epoch 453/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.3901 - val_loss: 1.1851 - val_accuracy: 0.3652 Epoch 454/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.3775 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 455/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4069 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 456/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4006 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 457/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.3922 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 458/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.3964 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 459/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2237 - accuracy: 0.4111 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 460/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2252 - accuracy: 0.3870 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 461/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4017 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 462/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.3743 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 463/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2246 - accuracy: 0.3922 - val_loss: 1.1803 - val_accuracy: 0.3652 Epoch 464/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4027 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 465/500 96/96 [==============================] - 1s 8ms/step - loss: 1.2237 - accuracy: 0.4248 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 466/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3891 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 467/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2240 - accuracy: 0.4259 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 468/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.4080 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 469/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.4017 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 470/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.3943 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 471/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2247 - accuracy: 0.4038 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 472/500 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3807 - val_loss: 1.1868 - val_accuracy: 0.3652 Epoch 473/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2241 - accuracy: 0.4259 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 474/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.3828 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 475/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4132 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 476/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3680 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 477/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4059 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 478/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3870 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 479/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2245 - accuracy: 0.4069 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 480/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2240 - accuracy: 0.3985 - val_loss: 1.1849 - val_accuracy: 0.3652 Epoch 481/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4101 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 482/500 96/96 [==============================] - 0s 5ms/step - loss: 1.2244 - accuracy: 0.3954 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 483/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2239 - accuracy: 0.4185 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 484/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.3964 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 485/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3891 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 486/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4090 - val_loss: 1.1696 - val_accuracy: 0.4706 Epoch 487/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.3996 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 488/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2258 - accuracy: 0.3849 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 489/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2246 - accuracy: 0.3807 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 490/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2248 - accuracy: 0.4017 - val_loss: 1.1749 - val_accuracy: 0.3652 Epoch 491/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.3838 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 492/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3922 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 493/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2247 - accuracy: 0.4143 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 494/500 96/96 [==============================] - 1s 7ms/step - loss: 1.2244 - accuracy: 0.4059 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 495/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2248 - accuracy: 0.4038 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 496/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2252 - accuracy: 0.4038 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 497/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2251 - accuracy: 0.4017 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 498/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4048 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 499/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2253 - accuracy: 0.3975 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 500/500 96/96 [==============================] - 1s 5ms/step - loss: 1.2243 - accuracy: 0.4080 - val_loss: 1.1899 - val_accuracy: 0.3652 13/13 [==============================] - 0s 3ms/step
print(RESULTS)
NO OF EPOCHS MODEL SCORE TRAINING ACCURACY TESTING ACCURACY 0 200.0 -0.010861 0.434280 0.470588 1 300.0 -0.007187 0.432177 0.470588 2 400.0 -0.002008 0.436383 0.470588 3 500.0 -0.010811 0.439537 0.470588
# TRY THE DESIGN WITH VARIOUS OPTIMIZERS AND COMPILE
OPT = ['Adam', 'RMSprop', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl']
RESULTS = pd.DataFrame()
for i in OPT:
NN_MODEL.compile(loss='categorical_crossentropy', optimizer=i, metrics=['accuracy'])
LR_HIST = NN_MODEL.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10).history
y_pred = NN_MODEL.predict(x_test_s)
SCORE = metrics.r2_score(y_test_c,y_pred)
MAX_ACC = np.max(LR_HIST['accuracy'])
MAX_VAL_ACC = np.max(LR_HIST['val_accuracy'])
RESULTS = RESULTS.append(pd.Series([i,SCORE,MAX_ACC,MAX_VAL_ACC]),ignore_index=True)
RESULTS.columns = ['NO OF EPOCHS','MODEL SCORE','TRAINING ACCURACY','TESTING ACCURACY']
Epoch 1/100 96/96 [==============================] - 2s 9ms/step - loss: 1.2258 - accuracy: 0.4059 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2234 - accuracy: 0.3901 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.4059 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.4069 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4059 - val_loss: 1.1749 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4059 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2232 - accuracy: 0.4027 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.4059 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1747 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3933 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3975 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.3817 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3985 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.3701 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3849 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3964 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3954 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3712 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3807 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3870 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4069 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3870 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.3733 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1740 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4006 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4048 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3838 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4027 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4048 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3922 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1741 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.4006 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3754 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4027 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4038 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3817 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3859 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3859 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4069 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3996 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4069 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3933 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4017 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4006 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1736 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3733 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3901 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3870 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3985 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1740 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3912 - val_loss: 1.1740 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3828 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3849 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3891 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3743 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3880 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3859 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3754 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3754 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3870 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3764 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3838 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3922 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3764 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3901 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3985 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3849 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3964 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3901 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3985 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3817 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4080 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3954 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.3985 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3922 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3996 - val_loss: 1.1723 - val_accuracy: 0.4706 13/13 [==============================] - 0s 4ms/step Epoch 1/100 96/96 [==============================] - 3s 9ms/step - loss: 1.2225 - accuracy: 0.4017 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.4080 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4006 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3922 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3933 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4017 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4017 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3880 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3943 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3943 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3985 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3933 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3891 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3817 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2229 - accuracy: 0.4090 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2229 - accuracy: 0.3975 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2228 - accuracy: 0.3943 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3828 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2225 - accuracy: 0.3996 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 11ms/step - loss: 1.2230 - accuracy: 0.3891 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 22/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2228 - accuracy: 0.3722 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.3796 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3985 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3964 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2227 - accuracy: 0.3838 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3901 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4048 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2229 - accuracy: 0.3859 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2227 - accuracy: 0.3964 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3849 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2227 - accuracy: 0.4080 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3943 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3754 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.4069 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4017 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4017 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.4090 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3922 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3943 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.4059 - val_loss: 1.1714 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3985 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3817 - val_loss: 1.1714 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.3996 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3754 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2238 - accuracy: 0.4017 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2231 - accuracy: 0.4059 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.3891 - val_loss: 1.1745 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2234 - accuracy: 0.4059 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.4059 - val_loss: 1.1717 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3775 - val_loss: 1.1714 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4017 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3880 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3964 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3912 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2226 - accuracy: 0.3838 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2227 - accuracy: 0.4017 - val_loss: 1.1714 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2227 - accuracy: 0.3785 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2231 - accuracy: 0.3849 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2227 - accuracy: 0.4038 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 65/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2226 - accuracy: 0.3943 - val_loss: 1.1716 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3985 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2227 - accuracy: 0.4059 - val_loss: 1.1717 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2226 - accuracy: 0.3943 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3922 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.4059 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3891 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2231 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.4006 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3985 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.4059 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3880 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3880 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3807 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2227 - accuracy: 0.3922 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.4038 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.4006 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3954 - val_loss: 1.1716 - val_accuracy: 0.3652 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.3775 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2231 - accuracy: 0.3996 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4038 - val_loss: 1.1717 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3849 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3954 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4038 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3933 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3754 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.4017 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2226 - accuracy: 0.3764 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2233 - accuracy: 0.3901 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2231 - accuracy: 0.3870 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3807 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.3880 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2228 - accuracy: 0.4017 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2231 - accuracy: 0.3796 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.3964 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2230 - accuracy: 0.4048 - val_loss: 1.1721 - val_accuracy: 0.3652 13/13 [==============================] - 0s 3ms/step Epoch 1/100 96/96 [==============================] - 2s 9ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 13/13 [==============================] - 0s 3ms/step Epoch 1/100 96/96 [==============================] - 2s 9ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 13/13 [==============================] - 0s 6ms/step Epoch 1/100 96/96 [==============================] - 3s 13ms/step - loss: 1.2226 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2225 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2224 - accuracy: 0.3764 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.4006 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2224 - accuracy: 0.3901 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2223 - accuracy: 0.4017 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3838 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.3933 - val_loss: 1.1719 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4027 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3828 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3912 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3880 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3838 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3901 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2222 - accuracy: 0.3901 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3817 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3985 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3901 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3975 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4038 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3828 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3933 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3922 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3996 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4017 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3828 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.3838 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4038 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4059 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2219 - accuracy: 0.4143 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2218 - accuracy: 0.3922 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4006 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2220 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.3943 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4048 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.3828 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.3964 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.3828 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.3975 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.4038 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2219 - accuracy: 0.3964 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2218 - accuracy: 0.4048 - val_loss: 1.1721 - val_accuracy: 0.4706 13/13 [==============================] - 0s 3ms/step Epoch 1/100 96/96 [==============================] - 4s 10ms/step - loss: 1.2222 - accuracy: 0.3943 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 2/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3954 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3901 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3975 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 7/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3985 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3975 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3796 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3943 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3975 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3785 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2222 - accuracy: 0.3922 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3933 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2223 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3754 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3985 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3975 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2224 - accuracy: 0.4006 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3891 - val_loss: 1.1720 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2222 - accuracy: 0.3880 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3933 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3943 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3954 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3880 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2221 - accuracy: 0.3985 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2221 - accuracy: 0.3712 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2222 - accuracy: 0.3807 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 48/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2220 - accuracy: 0.3954 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2222 - accuracy: 0.3849 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1716 - val_accuracy: 0.4706 Epoch 52/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3891 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3870 - val_loss: 1.1722 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3891 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3817 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3670 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3933 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3849 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4059 - val_loss: 1.1732 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2223 - accuracy: 0.3964 - val_loss: 1.1725 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3922 - val_loss: 1.1727 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3807 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3870 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2221 - accuracy: 0.3985 - val_loss: 1.1726 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1728 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3796 - val_loss: 1.1730 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.3817 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4048 - val_loss: 1.1728 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3691 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2222 - accuracy: 0.4059 - val_loss: 1.1733 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3891 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3859 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3807 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3901 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.4048 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3996 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 88/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3870 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3807 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3985 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.4048 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.4048 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.4006 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3964 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2221 - accuracy: 0.3922 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3807 - val_loss: 1.1723 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2220 - accuracy: 0.3828 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2221 - accuracy: 0.3922 - val_loss: 1.1728 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.3975 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2220 - accuracy: 0.4059 - val_loss: 1.1730 - val_accuracy: 0.3652 13/13 [==============================] - 0s 4ms/step Epoch 1/100 96/96 [==============================] - 3s 14ms/step - loss: 2.1336 - accuracy: 0.4059 - val_loss: 2.1239 - val_accuracy: 0.4706 Epoch 2/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1297 - accuracy: 0.4048 - val_loss: 2.1154 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1224 - accuracy: 0.4048 - val_loss: 2.1088 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1166 - accuracy: 0.4048 - val_loss: 2.1032 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1115 - accuracy: 0.4048 - val_loss: 2.0983 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1070 - accuracy: 0.4048 - val_loss: 2.0939 - val_accuracy: 0.4706 Epoch 7/100 96/96 [==============================] - 1s 9ms/step - loss: 2.1029 - accuracy: 0.4048 - val_loss: 2.0899 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0992 - accuracy: 0.4048 - val_loss: 2.0861 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0956 - accuracy: 0.4048 - val_loss: 2.0826 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0923 - accuracy: 0.4048 - val_loss: 2.0793 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0892 - accuracy: 0.4048 - val_loss: 2.0761 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0862 - accuracy: 0.4048 - val_loss: 2.0731 - val_accuracy: 0.4706 Epoch 13/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0834 - accuracy: 0.4048 - val_loss: 2.0702 - val_accuracy: 0.4706 Epoch 14/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0807 - accuracy: 0.4048 - val_loss: 2.0675 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0780 - accuracy: 0.4048 - val_loss: 2.0648 - val_accuracy: 0.4706 Epoch 16/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0755 - accuracy: 0.4048 - val_loss: 2.0623 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0731 - accuracy: 0.4048 - val_loss: 2.0598 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0707 - accuracy: 0.4048 - val_loss: 2.0574 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0684 - accuracy: 0.4048 - val_loss: 2.0551 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0662 - accuracy: 0.4048 - val_loss: 2.0528 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0640 - accuracy: 0.4048 - val_loss: 2.0506 - val_accuracy: 0.4706 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0619 - accuracy: 0.4048 - val_loss: 2.0485 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0599 - accuracy: 0.4048 - val_loss: 2.0464 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0579 - accuracy: 0.4048 - val_loss: 2.0443 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 11ms/step - loss: 2.0559 - accuracy: 0.4048 - val_loss: 2.0423 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0540 - accuracy: 0.4048 - val_loss: 2.0404 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0521 - accuracy: 0.4048 - val_loss: 2.0384 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0503 - accuracy: 0.4048 - val_loss: 2.0366 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0485 - accuracy: 0.4048 - val_loss: 2.0347 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0467 - accuracy: 0.4048 - val_loss: 2.0329 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0450 - accuracy: 0.4048 - val_loss: 2.0311 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0433 - accuracy: 0.4048 - val_loss: 2.0294 - val_accuracy: 0.4706 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0416 - accuracy: 0.4048 - val_loss: 2.0277 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0400 - accuracy: 0.4048 - val_loss: 2.0260 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0383 - accuracy: 0.4048 - val_loss: 2.0243 - val_accuracy: 0.4706 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0367 - accuracy: 0.4048 - val_loss: 2.0227 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0352 - accuracy: 0.4048 - val_loss: 2.0211 - val_accuracy: 0.4706 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0336 - accuracy: 0.4048 - val_loss: 2.0195 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0321 - accuracy: 0.4048 - val_loss: 2.0180 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0306 - accuracy: 0.4048 - val_loss: 2.0164 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0291 - accuracy: 0.4048 - val_loss: 2.0149 - val_accuracy: 0.4706 Epoch 42/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0277 - accuracy: 0.4048 - val_loss: 2.0134 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0262 - accuracy: 0.4048 - val_loss: 2.0119 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0248 - accuracy: 0.4048 - val_loss: 2.0105 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0234 - accuracy: 0.4048 - val_loss: 2.0090 - val_accuracy: 0.4706 Epoch 46/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0220 - accuracy: 0.4048 - val_loss: 2.0076 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0206 - accuracy: 0.4048 - val_loss: 2.0062 - val_accuracy: 0.4706 Epoch 48/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0193 - accuracy: 0.4048 - val_loss: 2.0048 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0180 - accuracy: 0.4048 - val_loss: 2.0034 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0167 - accuracy: 0.4048 - val_loss: 2.0021 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0153 - accuracy: 0.4048 - val_loss: 2.0007 - val_accuracy: 0.4706 Epoch 52/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0141 - accuracy: 0.4048 - val_loss: 1.9994 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0128 - accuracy: 0.4048 - val_loss: 1.9981 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0115 - accuracy: 0.4048 - val_loss: 1.9968 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0103 - accuracy: 0.4048 - val_loss: 1.9955 - val_accuracy: 0.4706 Epoch 56/100 96/96 [==============================] - 1s 9ms/step - loss: 2.0090 - accuracy: 0.4048 - val_loss: 1.9942 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0078 - accuracy: 0.4048 - val_loss: 1.9930 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 8ms/step - loss: 2.0066 - accuracy: 0.4048 - val_loss: 1.9917 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 7ms/step - loss: 2.0054 - accuracy: 0.4048 - val_loss: 1.9905 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0042 - accuracy: 0.4048 - val_loss: 1.9893 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0030 - accuracy: 0.4048 - val_loss: 1.9881 - val_accuracy: 0.4706 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0018 - accuracy: 0.4048 - val_loss: 1.9869 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 2.0007 - accuracy: 0.4048 - val_loss: 1.9857 - val_accuracy: 0.4706 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9995 - accuracy: 0.4048 - val_loss: 1.9845 - val_accuracy: 0.4706 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9984 - accuracy: 0.4048 - val_loss: 1.9833 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9973 - accuracy: 0.4048 - val_loss: 1.9821 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9961 - accuracy: 0.4048 - val_loss: 1.9810 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9950 - accuracy: 0.4048 - val_loss: 1.9799 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9939 - accuracy: 0.4048 - val_loss: 1.9787 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9929 - accuracy: 0.4048 - val_loss: 1.9776 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9918 - accuracy: 0.4048 - val_loss: 1.9765 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9907 - accuracy: 0.4048 - val_loss: 1.9754 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9896 - accuracy: 0.4048 - val_loss: 1.9743 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9886 - accuracy: 0.4048 - val_loss: 1.9732 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9875 - accuracy: 0.4048 - val_loss: 1.9721 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9864 - accuracy: 0.4048 - val_loss: 1.9710 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9853 - accuracy: 0.4048 - val_loss: 1.9698 - val_accuracy: 0.4706 Epoch 78/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9843 - accuracy: 0.4048 - val_loss: 1.9687 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9832 - accuracy: 0.4048 - val_loss: 1.9676 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9821 - accuracy: 0.4048 - val_loss: 1.9665 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9810 - accuracy: 0.4048 - val_loss: 1.9653 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9798 - accuracy: 0.4048 - val_loss: 1.9641 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9787 - accuracy: 0.4048 - val_loss: 1.9629 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9775 - accuracy: 0.4048 - val_loss: 1.9616 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9762 - accuracy: 0.4048 - val_loss: 1.9603 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9749 - accuracy: 0.4048 - val_loss: 1.9589 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9735 - accuracy: 0.4048 - val_loss: 1.9574 - val_accuracy: 0.4706 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9720 - accuracy: 0.4048 - val_loss: 1.9558 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9705 - accuracy: 0.4048 - val_loss: 1.9542 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9688 - accuracy: 0.4048 - val_loss: 1.9524 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9670 - accuracy: 0.4048 - val_loss: 1.9504 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9651 - accuracy: 0.4048 - val_loss: 1.9484 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9630 - accuracy: 0.4048 - val_loss: 1.9461 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.9607 - accuracy: 0.4048 - val_loss: 1.9437 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 7ms/step - loss: 1.9583 - accuracy: 0.4048 - val_loss: 1.9411 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 9ms/step - loss: 1.9558 - accuracy: 0.4048 - val_loss: 1.9384 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 8ms/step - loss: 1.9531 - accuracy: 0.4048 - val_loss: 1.9355 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 8ms/step - loss: 1.9501 - accuracy: 0.4048 - val_loss: 1.9323 - val_accuracy: 0.4706 Epoch 99/100 96/96 [==============================] - 1s 9ms/step - loss: 1.9470 - accuracy: 0.4048 - val_loss: 1.9290 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 9ms/step - loss: 1.9437 - accuracy: 0.4048 - val_loss: 1.9255 - val_accuracy: 0.4706 13/13 [==============================] - 0s 3ms/step
print(RESULTS)
NO OF EPOCHS MODEL SCORE TRAINING ACCURACY TESTING ACCURACY 0 Adam -0.004342 0.407992 0.470588 1 RMSprop -0.004092 0.409043 0.470588 2 Adadelta -0.004088 0.405889 0.365196 3 Adagrad -0.004072 0.405889 0.365196 4 Adamax -0.004241 0.414301 0.470588 5 Nadam -0.004594 0.405889 0.470588 6 Ftrl -0.323946 0.405889 0.470588
* APART FROM SGD OPTIMIZER, WE ARE NOT GETTING GOOD RESULTS AFROM OTHER OPTIMIZER FUNCTIONS.
* WE WILL STICK WITH THE SGD OPTIMIZER FOR THE DESIGN.
# TRY WITH SGD OPTIMIZER AND USE DIFFERENT LEARNING RATES TO TRAIN AND TEST THE MODEL.
RESULTS = pd.DataFrame()
LR = np.arange(0.01,0.11,0.01)
for i in LR:
sgd = SGD(lr=i)
NN_MODEL.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
NN_MODEL.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
y_pred = NN_MODEL.predict(x_test_s)
SCORE = metrics.r2_score(y_test_c,y_pred)
RESULTS = RESULTS.append(pd.Series([i, SCORE]),ignore_index=True)
RESULTS.columns = ['LEARNING RATE', 'MODEL SCORE']
Epoch 1/100 96/96 [==============================] - 2s 13ms/step - loss: 1.4754 - accuracy: 0.3985 - val_loss: 1.3608 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 5ms/step - loss: 1.3611 - accuracy: 0.3901 - val_loss: 1.2829 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.3149 - accuracy: 0.4006 - val_loss: 1.2490 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2931 - accuracy: 0.3796 - val_loss: 1.2293 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2796 - accuracy: 0.4080 - val_loss: 1.2187 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2709 - accuracy: 0.3807 - val_loss: 1.2113 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2638 - accuracy: 0.4027 - val_loss: 1.2078 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2589 - accuracy: 0.3933 - val_loss: 1.1979 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2548 - accuracy: 0.4038 - val_loss: 1.2009 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2519 - accuracy: 0.3964 - val_loss: 1.1923 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2490 - accuracy: 0.4111 - val_loss: 1.1914 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2471 - accuracy: 0.3785 - val_loss: 1.1883 - val_accuracy: 0.4706 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2451 - accuracy: 0.3828 - val_loss: 1.1877 - val_accuracy: 0.4706 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2433 - accuracy: 0.4069 - val_loss: 1.1832 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2425 - accuracy: 0.4101 - val_loss: 1.1843 - val_accuracy: 0.4706 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2412 - accuracy: 0.3743 - val_loss: 1.1853 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2406 - accuracy: 0.3733 - val_loss: 1.1879 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2392 - accuracy: 0.4069 - val_loss: 1.1816 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2387 - accuracy: 0.3838 - val_loss: 1.1853 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2379 - accuracy: 0.3796 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2372 - accuracy: 0.4048 - val_loss: 1.1836 - val_accuracy: 0.4706 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2366 - accuracy: 0.3849 - val_loss: 1.1828 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2356 - accuracy: 0.3912 - val_loss: 1.1805 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2356 - accuracy: 0.3712 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2348 - accuracy: 0.4069 - val_loss: 1.1780 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2350 - accuracy: 0.3817 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2342 - accuracy: 0.3807 - val_loss: 1.1855 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2336 - accuracy: 0.3985 - val_loss: 1.1777 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2320 - accuracy: 0.4290 - val_loss: 1.1898 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2337 - accuracy: 0.4017 - val_loss: 1.1792 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2327 - accuracy: 0.4006 - val_loss: 1.1796 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2323 - accuracy: 0.3985 - val_loss: 1.1757 - val_accuracy: 0.4706 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2327 - accuracy: 0.3922 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2327 - accuracy: 0.4017 - val_loss: 1.1753 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2317 - accuracy: 0.4143 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2317 - accuracy: 0.3996 - val_loss: 1.1776 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2316 - accuracy: 0.3807 - val_loss: 1.1800 - val_accuracy: 0.4706 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2312 - accuracy: 0.3975 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2313 - accuracy: 0.4038 - val_loss: 1.1793 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2307 - accuracy: 0.3922 - val_loss: 1.1754 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2306 - accuracy: 0.4111 - val_loss: 1.1784 - val_accuracy: 0.4706 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2306 - accuracy: 0.4038 - val_loss: 1.1782 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2302 - accuracy: 0.4059 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2304 - accuracy: 0.3859 - val_loss: 1.1765 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2302 - accuracy: 0.3891 - val_loss: 1.1767 - val_accuracy: 0.4706 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2300 - accuracy: 0.3954 - val_loss: 1.1739 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2300 - accuracy: 0.4017 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2298 - accuracy: 0.3891 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2295 - accuracy: 0.3933 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2296 - accuracy: 0.4090 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2295 - accuracy: 0.3943 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2290 - accuracy: 0.4048 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2294 - accuracy: 0.4069 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3943 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.4269 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2290 - accuracy: 0.3849 - val_loss: 1.1744 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3922 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3912 - val_loss: 1.1760 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3870 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3996 - val_loss: 1.1769 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.3933 - val_loss: 1.1802 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2289 - accuracy: 0.3996 - val_loss: 1.1813 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.4048 - val_loss: 1.1746 - val_accuracy: 0.4706 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3807 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.4006 - val_loss: 1.1742 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3964 - val_loss: 1.1714 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3996 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.4111 - val_loss: 1.1751 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3954 - val_loss: 1.1734 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3828 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3859 - val_loss: 1.1789 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3943 - val_loss: 1.1761 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4017 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4175 - val_loss: 1.1733 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3943 - val_loss: 1.1773 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4059 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3996 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.3996 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3975 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4006 - val_loss: 1.1750 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2276 - accuracy: 0.3838 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4069 - val_loss: 1.1748 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3880 - val_loss: 1.1729 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3985 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4101 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4027 - val_loss: 1.1795 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3922 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3849 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4080 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.3954 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3807 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3922 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3901 - val_loss: 1.1736 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3880 - val_loss: 1.1744 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4069 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4017 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3964 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3933 - val_loss: 1.1723 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.3943 - val_loss: 1.1702 - val_accuracy: 0.4706 13/13 [==============================] - 0s 3ms/step Epoch 1/100 96/96 [==============================] - 2s 10ms/step - loss: 1.2277 - accuracy: 0.3954 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3870 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3859 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4048 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3943 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2265 - accuracy: 0.4269 - val_loss: 1.1739 - val_accuracy: 0.4706 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3817 - val_loss: 1.1876 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3870 - val_loss: 1.1811 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3775 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3701 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4017 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4196 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3975 - val_loss: 1.1743 - val_accuracy: 0.4706 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4038 - val_loss: 1.1819 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3859 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4122 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4238 - val_loss: 1.1893 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3775 - val_loss: 1.1790 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3743 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.3901 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3764 - val_loss: 1.1836 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4206 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3870 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3828 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3807 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.3922 - val_loss: 1.1856 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.4206 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4259 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4185 - val_loss: 1.1744 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4048 - val_loss: 1.1996 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3985 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4132 - val_loss: 1.1903 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3785 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4111 - val_loss: 1.1712 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4090 - val_loss: 1.1713 - val_accuracy: 0.4706 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3796 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4027 - val_loss: 1.1831 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4038 - val_loss: 1.1908 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.4090 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3943 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.3922 - val_loss: 1.1797 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3901 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4154 - val_loss: 1.1747 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3964 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3743 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 46/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3849 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4027 - val_loss: 1.1916 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4238 - val_loss: 1.1904 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3870 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3922 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3954 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4069 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3912 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.4017 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4143 - val_loss: 1.2014 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.4069 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.4132 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3943 - val_loss: 1.1887 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4154 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2250 - accuracy: 0.3922 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3996 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4101 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3859 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4017 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3817 - val_loss: 1.1783 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.3933 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.4006 - val_loss: 1.1925 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3807 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.4101 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.3901 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.3943 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.3975 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2260 - accuracy: 0.3996 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.4101 - val_loss: 1.1958 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2264 - accuracy: 0.3922 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3912 - val_loss: 1.1823 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4101 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4027 - val_loss: 1.1772 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3901 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3943 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4185 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3817 - val_loss: 1.1754 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4027 - val_loss: 1.1749 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2244 - accuracy: 0.4164 - val_loss: 1.1731 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4090 - val_loss: 1.1730 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4090 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2236 - accuracy: 0.4280 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3943 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3870 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3638 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4006 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4143 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4090 - val_loss: 1.1744 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.3964 - val_loss: 1.1812 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1912 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4069 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4059 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2279 - accuracy: 0.3985 - val_loss: 1.1878 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3849 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3933 - val_loss: 1.1683 - val_accuracy: 0.4706 13/13 [==============================] - 0s 4ms/step Epoch 1/100 96/96 [==============================] - 3s 12ms/step - loss: 1.2281 - accuracy: 0.3912 - val_loss: 1.2003 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2281 - accuracy: 0.3975 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2235 - accuracy: 0.4196 - val_loss: 1.2432 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2291 - accuracy: 0.4027 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.4048 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2259 - accuracy: 0.4206 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4132 - val_loss: 1.1887 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2211 - accuracy: 0.4416 - val_loss: 1.1950 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3880 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3975 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3996 - val_loss: 1.1918 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4069 - val_loss: 1.2189 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2295 - accuracy: 0.4090 - val_loss: 1.1858 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.4206 - val_loss: 1.2039 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2293 - accuracy: 0.3807 - val_loss: 1.1877 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2281 - accuracy: 0.4038 - val_loss: 1.1912 - val_accuracy: 0.3652 Epoch 17/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2298 - accuracy: 0.3922 - val_loss: 1.1992 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.4017 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2281 - accuracy: 0.3764 - val_loss: 1.1691 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4017 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2287 - accuracy: 0.4059 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 22/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.3922 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.4090 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2290 - accuracy: 0.3996 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4080 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3722 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4038 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3975 - val_loss: 1.1761 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3922 - val_loss: 1.1764 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4038 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3880 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2287 - accuracy: 0.3764 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3701 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3985 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4027 - val_loss: 1.1879 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3933 - val_loss: 1.1952 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4017 - val_loss: 1.1853 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.4017 - val_loss: 1.2057 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4101 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3701 - val_loss: 1.1810 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2288 - accuracy: 0.4101 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 42/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.4143 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.4090 - val_loss: 1.1989 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.3985 - val_loss: 1.1888 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2287 - accuracy: 0.3880 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3933 - val_loss: 1.1735 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.4080 - val_loss: 1.2066 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3817 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2280 - accuracy: 0.4006 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2294 - accuracy: 0.3680 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3743 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2296 - accuracy: 0.3922 - val_loss: 1.1958 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3996 - val_loss: 1.1894 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4185 - val_loss: 1.2184 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4111 - val_loss: 1.1726 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.3996 - val_loss: 1.2044 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3964 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3828 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3817 - val_loss: 1.1851 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4132 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4069 - val_loss: 1.2038 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3964 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2263 - accuracy: 0.4217 - val_loss: 1.1956 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3880 - val_loss: 1.1895 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4280 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4069 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3712 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3775 - val_loss: 1.1901 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2300 - accuracy: 0.3617 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.3785 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3922 - val_loss: 1.1780 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.3954 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.4059 - val_loss: 1.1821 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.4111 - val_loss: 1.1887 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2280 - accuracy: 0.3964 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4122 - val_loss: 1.1733 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.3891 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.4101 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.3975 - val_loss: 1.1851 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2288 - accuracy: 0.3838 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3891 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4111 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2250 - accuracy: 0.4132 - val_loss: 1.1730 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3933 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4059 - val_loss: 1.1713 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4069 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4038 - val_loss: 1.1967 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.4006 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4185 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2248 - accuracy: 0.4059 - val_loss: 1.1941 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4217 - val_loss: 1.1724 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4038 - val_loss: 1.1911 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3985 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2251 - accuracy: 0.4101 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2237 - accuracy: 0.4164 - val_loss: 1.1794 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3912 - val_loss: 1.1742 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4017 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.3922 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3849 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3996 - val_loss: 1.1674 - val_accuracy: 0.4706 13/13 [==============================] - 0s 3ms/step Epoch 1/100 96/96 [==============================] - 2s 11ms/step - loss: 1.2293 - accuracy: 0.3796 - val_loss: 1.1973 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.3975 - val_loss: 1.1894 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2302 - accuracy: 0.3785 - val_loss: 1.2089 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.4132 - val_loss: 1.2118 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.4280 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.4322 - val_loss: 1.2013 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2295 - accuracy: 0.3785 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2255 - accuracy: 0.4217 - val_loss: 1.2010 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2274 - accuracy: 0.4027 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2288 - accuracy: 0.3880 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4290 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.4059 - val_loss: 1.2022 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2295 - accuracy: 0.4069 - val_loss: 1.1898 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.4006 - val_loss: 1.1983 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.4027 - val_loss: 1.1778 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.3985 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2286 - accuracy: 0.3901 - val_loss: 1.1918 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2287 - accuracy: 0.3954 - val_loss: 1.2009 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4154 - val_loss: 1.1850 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3901 - val_loss: 1.1861 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2264 - accuracy: 0.3933 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3870 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3817 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4059 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4038 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2287 - accuracy: 0.3912 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4017 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2293 - accuracy: 0.3817 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4122 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.4017 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4175 - val_loss: 1.1744 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.3849 - val_loss: 1.1969 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4143 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2287 - accuracy: 0.4069 - val_loss: 1.1938 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4048 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3733 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4143 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3912 - val_loss: 1.1919 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4038 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3891 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.4048 - val_loss: 1.1837 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.3964 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.3764 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.4059 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2249 - accuracy: 0.4090 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.4132 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4080 - val_loss: 1.1771 - val_accuracy: 0.4706 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4069 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2279 - accuracy: 0.3817 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.3985 - val_loss: 1.2004 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3933 - val_loss: 1.1935 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.4101 - val_loss: 1.1929 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4395 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3912 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4069 - val_loss: 1.1778 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3964 - val_loss: 1.1791 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.4090 - val_loss: 1.1890 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3880 - val_loss: 1.1750 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3922 - val_loss: 1.1942 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4048 - val_loss: 1.1833 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4111 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.4038 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4164 - val_loss: 1.1939 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2289 - accuracy: 0.3933 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.3964 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.4101 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2286 - accuracy: 0.3775 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2250 - accuracy: 0.4206 - val_loss: 1.2080 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2290 - accuracy: 0.4206 - val_loss: 1.1747 - val_accuracy: 0.3652 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2279 - accuracy: 0.4111 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.4048 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.4132 - val_loss: 1.1648 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.3859 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.3817 - val_loss: 1.1777 - val_accuracy: 0.3652 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3985 - val_loss: 1.1736 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4038 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2296 - accuracy: 0.3838 - val_loss: 1.1798 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3943 - val_loss: 1.1691 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3817 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.4101 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3870 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4090 - val_loss: 1.1793 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3922 - val_loss: 1.1921 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4122 - val_loss: 1.1996 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4175 - val_loss: 1.1792 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4059 - val_loss: 1.1756 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3754 - val_loss: 1.1887 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3954 - val_loss: 1.1724 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3817 - val_loss: 1.1751 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.3891 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.3996 - val_loss: 1.1975 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.4227 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.3975 - val_loss: 1.2062 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.3796 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4196 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3817 - val_loss: 1.1880 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2290 - accuracy: 0.3796 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 99/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.4038 - val_loss: 1.1771 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2284 - accuracy: 0.3764 - val_loss: 1.1720 - val_accuracy: 0.4706 13/13 [==============================] - 0s 5ms/step Epoch 1/100 96/96 [==============================] - 3s 12ms/step - loss: 1.2266 - accuracy: 0.3912 - val_loss: 1.1971 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4164 - val_loss: 1.2073 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2302 - accuracy: 0.3796 - val_loss: 1.1766 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3912 - val_loss: 1.1892 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3996 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4196 - val_loss: 1.1753 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4154 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2270 - accuracy: 0.4069 - val_loss: 1.2027 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2292 - accuracy: 0.3996 - val_loss: 1.1896 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3859 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4006 - val_loss: 1.1930 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3922 - val_loss: 1.1990 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2295 - accuracy: 0.3764 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2289 - accuracy: 0.3901 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.3817 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4017 - val_loss: 1.1980 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.4027 - val_loss: 1.1959 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3954 - val_loss: 1.1891 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4048 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4227 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2276 - accuracy: 0.3975 - val_loss: 1.1806 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2293 - accuracy: 0.3838 - val_loss: 1.1854 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3880 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4154 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4175 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.4059 - val_loss: 1.1964 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.4006 - val_loss: 1.1646 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3996 - val_loss: 1.1851 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2295 - accuracy: 0.3901 - val_loss: 1.1829 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4132 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.4132 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.3849 - val_loss: 1.1968 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.4122 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3796 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.4038 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3996 - val_loss: 1.1801 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2275 - accuracy: 0.4038 - val_loss: 1.1792 - val_accuracy: 0.3652 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3954 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3849 - val_loss: 1.1716 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4101 - val_loss: 1.1887 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3964 - val_loss: 1.1645 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4090 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3775 - val_loss: 1.1694 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.4090 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4101 - val_loss: 1.1882 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3985 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.4006 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.4164 - val_loss: 1.1915 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.3785 - val_loss: 1.1758 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4006 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3985 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2274 - accuracy: 0.3996 - val_loss: 1.1648 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.4206 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2245 - accuracy: 0.4269 - val_loss: 1.1973 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.4069 - val_loss: 1.1734 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2234 - accuracy: 0.4290 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.4038 - val_loss: 1.1838 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2292 - accuracy: 0.3870 - val_loss: 1.1695 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.4111 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.4111 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 62/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.4238 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2277 - accuracy: 0.3775 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.4017 - val_loss: 1.1840 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4017 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3912 - val_loss: 1.1692 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.3775 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3807 - val_loss: 1.1828 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3754 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4111 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.3975 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.3996 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.4027 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4132 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.3933 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.3880 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4017 - val_loss: 1.1928 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.3996 - val_loss: 1.1641 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3901 - val_loss: 1.1936 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4132 - val_loss: 1.1729 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4038 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4048 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3964 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4227 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4059 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4101 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.4111 - val_loss: 1.1970 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.4069 - val_loss: 1.1851 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2285 - accuracy: 0.4080 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2259 - accuracy: 0.4080 - val_loss: 1.1701 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4101 - val_loss: 1.1920 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.4206 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.4080 - val_loss: 1.1884 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3954 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.4185 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2281 - accuracy: 0.3743 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2284 - accuracy: 0.3922 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2280 - accuracy: 0.3933 - val_loss: 1.1703 - val_accuracy: 0.4706 Epoch 99/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2265 - accuracy: 0.4038 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.3807 - val_loss: 1.1656 - val_accuracy: 0.4706 13/13 [==============================] - 0s 5ms/step Epoch 1/100 96/96 [==============================] - 3s 10ms/step - loss: 1.2256 - accuracy: 0.4038 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 2/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.4101 - val_loss: 1.1728 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4038 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3954 - val_loss: 1.1819 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.4164 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4217 - val_loss: 1.1956 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4132 - val_loss: 1.1977 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4090 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2290 - accuracy: 0.3859 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3817 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3849 - val_loss: 1.1890 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2276 - accuracy: 0.4090 - val_loss: 1.1784 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.4017 - val_loss: 1.1826 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2282 - accuracy: 0.3922 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2283 - accuracy: 0.3775 - val_loss: 1.1790 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2270 - accuracy: 0.4090 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.4090 - val_loss: 1.1894 - val_accuracy: 0.3652 Epoch 18/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2285 - accuracy: 0.3754 - val_loss: 1.1882 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2283 - accuracy: 0.3954 - val_loss: 1.1879 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3859 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2274 - accuracy: 0.3954 - val_loss: 1.1852 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.4006 - val_loss: 1.1870 - val_accuracy: 0.3652 Epoch 23/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.4175 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4196 - val_loss: 1.1746 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4132 - val_loss: 1.1998 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4101 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3964 - val_loss: 1.1854 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3922 - val_loss: 1.1914 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3943 - val_loss: 1.1825 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2295 - accuracy: 0.3796 - val_loss: 1.1775 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.4006 - val_loss: 1.2041 - val_accuracy: 0.3652 Epoch 32/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4006 - val_loss: 1.1751 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.4059 - val_loss: 1.1644 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.4111 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2288 - accuracy: 0.4027 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4006 - val_loss: 1.1761 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4080 - val_loss: 1.2071 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2293 - accuracy: 0.3859 - val_loss: 1.1646 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3975 - val_loss: 1.1836 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2297 - accuracy: 0.3838 - val_loss: 1.1796 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.4006 - val_loss: 1.1894 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3838 - val_loss: 1.1889 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4143 - val_loss: 1.1765 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3901 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4038 - val_loss: 1.1735 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3933 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4080 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4069 - val_loss: 1.1810 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3880 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3975 - val_loss: 1.1845 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4164 - val_loss: 1.1767 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3933 - val_loss: 1.1808 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4101 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4017 - val_loss: 1.1818 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4006 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3891 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4069 - val_loss: 1.1830 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4111 - val_loss: 1.1764 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2284 - accuracy: 0.3996 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2292 - accuracy: 0.3807 - val_loss: 1.1867 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3912 - val_loss: 1.1780 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.4006 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.3785 - val_loss: 1.2021 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2285 - accuracy: 0.3975 - val_loss: 1.1881 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3996 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2270 - accuracy: 0.4048 - val_loss: 1.2022 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.3828 - val_loss: 1.1853 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2281 - accuracy: 0.3870 - val_loss: 1.1861 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2255 - accuracy: 0.4059 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.4080 - val_loss: 1.1784 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.3785 - val_loss: 1.1940 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.4069 - val_loss: 1.1755 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4227 - val_loss: 1.1924 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4164 - val_loss: 1.1738 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4080 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4143 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4059 - val_loss: 1.1720 - val_accuracy: 0.4706 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4238 - val_loss: 1.1911 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4175 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3943 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3870 - val_loss: 1.1755 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3912 - val_loss: 1.1782 - val_accuracy: 0.3652 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4069 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3754 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2247 - accuracy: 0.4069 - val_loss: 1.2106 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.3901 - val_loss: 1.1986 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3817 - val_loss: 1.1823 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4048 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3943 - val_loss: 1.1704 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4006 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.3975 - val_loss: 1.1868 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4027 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2279 - accuracy: 0.4027 - val_loss: 1.1863 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.4280 - val_loss: 1.1736 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3964 - val_loss: 1.1788 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4017 - val_loss: 1.1993 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4227 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3954 - val_loss: 1.1936 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4185 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4196 - val_loss: 1.1762 - val_accuracy: 0.3652 13/13 [==============================] - 0s 4ms/step Epoch 1/100 96/96 [==============================] - 2s 10ms/step - loss: 1.2272 - accuracy: 0.4027 - val_loss: 1.2008 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2281 - accuracy: 0.3996 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.4101 - val_loss: 1.1877 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2305 - accuracy: 0.3901 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.4132 - val_loss: 1.1837 - val_accuracy: 0.4706 Epoch 6/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.3912 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 7/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2282 - accuracy: 0.3891 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2283 - accuracy: 0.3933 - val_loss: 1.1841 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2283 - accuracy: 0.4017 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.4027 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.3764 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.4185 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.3975 - val_loss: 1.1899 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.4143 - val_loss: 1.1918 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.4101 - val_loss: 1.1842 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3985 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.3912 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2288 - accuracy: 0.3859 - val_loss: 1.1904 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2277 - accuracy: 0.4196 - val_loss: 1.1874 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.4048 - val_loss: 1.1904 - val_accuracy: 0.3652 Epoch 21/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.4080 - val_loss: 1.2030 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3996 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4101 - val_loss: 1.1732 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3912 - val_loss: 1.1766 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4111 - val_loss: 1.2060 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2285 - accuracy: 0.4080 - val_loss: 1.1825 - val_accuracy: 0.3652 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1893 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.3985 - val_loss: 1.1841 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3880 - val_loss: 1.1839 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4280 - val_loss: 1.1773 - val_accuracy: 0.4706 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2259 - accuracy: 0.4038 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2283 - accuracy: 0.3912 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3996 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3912 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4038 - val_loss: 1.1896 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3785 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2287 - accuracy: 0.3607 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3838 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3975 - val_loss: 1.1637 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4059 - val_loss: 1.1710 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4027 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4059 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4101 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2248 - accuracy: 0.4206 - val_loss: 1.1754 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 9ms/step - loss: 1.2278 - accuracy: 0.3996 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 46/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.4101 - val_loss: 1.1812 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.4090 - val_loss: 1.1982 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.4206 - val_loss: 1.1717 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.3922 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.3975 - val_loss: 1.1747 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.3943 - val_loss: 1.2040 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3849 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 53/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4143 - val_loss: 1.2046 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.4027 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.4027 - val_loss: 1.1886 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2291 - accuracy: 0.3754 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3943 - val_loss: 1.1690 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4038 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.4048 - val_loss: 1.1837 - val_accuracy: 0.3652 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3912 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.4006 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3849 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3985 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 64/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.3985 - val_loss: 1.1815 - val_accuracy: 0.3652 Epoch 65/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3849 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2254 - accuracy: 0.4006 - val_loss: 1.1994 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3901 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.3985 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.3817 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.3964 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.3849 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2251 - accuracy: 0.4122 - val_loss: 1.1891 - val_accuracy: 0.3652 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2264 - accuracy: 0.4111 - val_loss: 1.1905 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2277 - accuracy: 0.3754 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4059 - val_loss: 1.1645 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4143 - val_loss: 1.1818 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.4143 - val_loss: 1.1752 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3975 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3659 - val_loss: 1.1776 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4027 - val_loss: 1.1790 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4175 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.4017 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4027 - val_loss: 1.1891 - val_accuracy: 0.3652 Epoch 84/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.4069 - val_loss: 1.1648 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2253 - accuracy: 0.4164 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4017 - val_loss: 1.1914 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2277 - accuracy: 0.3975 - val_loss: 1.1880 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3901 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3943 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.3807 - val_loss: 1.1975 - val_accuracy: 0.3652 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3975 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4006 - val_loss: 1.1894 - val_accuracy: 0.3652 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3943 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4048 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3922 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4017 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3785 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4038 - val_loss: 1.1814 - val_accuracy: 0.4706 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3870 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3954 - val_loss: 1.1737 - val_accuracy: 0.4706 13/13 [==============================] - 0s 4ms/step Epoch 1/100 96/96 [==============================] - 3s 11ms/step - loss: 1.2265 - accuracy: 0.3975 - val_loss: 1.1722 - val_accuracy: 0.4706 Epoch 2/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.4048 - val_loss: 1.1867 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.4111 - val_loss: 1.2005 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4248 - val_loss: 1.1721 - val_accuracy: 0.4706 Epoch 5/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2286 - accuracy: 0.3870 - val_loss: 1.1759 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.3859 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.3796 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2284 - accuracy: 0.3733 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2280 - accuracy: 0.3743 - val_loss: 1.1820 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2254 - accuracy: 0.4132 - val_loss: 1.1689 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3922 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4069 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.4027 - val_loss: 1.1748 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3880 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4132 - val_loss: 1.1687 - val_accuracy: 0.4706 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4027 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4059 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4048 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3796 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2243 - accuracy: 0.4227 - val_loss: 1.2081 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2290 - accuracy: 0.3964 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2229 - accuracy: 0.4154 - val_loss: 1.1669 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3943 - val_loss: 1.1668 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3891 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3954 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2258 - accuracy: 0.4080 - val_loss: 1.1651 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.4080 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3933 - val_loss: 1.1652 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4122 - val_loss: 1.1799 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4027 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4175 - val_loss: 1.1941 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3901 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3933 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2288 - accuracy: 0.3754 - val_loss: 1.1823 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.3954 - val_loss: 1.2017 - val_accuracy: 0.3652 Epoch 37/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.3880 - val_loss: 1.1994 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.4101 - val_loss: 1.1653 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 10ms/step - loss: 1.2273 - accuracy: 0.3880 - val_loss: 1.1699 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3828 - val_loss: 1.1707 - val_accuracy: 0.4706 Epoch 41/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2284 - accuracy: 0.3880 - val_loss: 1.1804 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2270 - accuracy: 0.4006 - val_loss: 1.1963 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.4006 - val_loss: 1.1857 - val_accuracy: 0.3652 Epoch 44/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2268 - accuracy: 0.3880 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3985 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2251 - accuracy: 0.4259 - val_loss: 1.1748 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.4122 - val_loss: 1.1876 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4069 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2293 - accuracy: 0.3628 - val_loss: 1.1853 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4143 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3880 - val_loss: 1.1815 - val_accuracy: 0.4706 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2274 - accuracy: 0.3933 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3870 - val_loss: 1.1697 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3954 - val_loss: 1.1718 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4280 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3796 - val_loss: 1.1952 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4017 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.4027 - val_loss: 1.1906 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.4038 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.4101 - val_loss: 1.1917 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3954 - val_loss: 1.2013 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.4101 - val_loss: 1.1693 - val_accuracy: 0.4706 Epoch 63/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.3912 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.4154 - val_loss: 1.1645 - val_accuracy: 0.4706 Epoch 65/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.4101 - val_loss: 1.1786 - val_accuracy: 0.4706 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2285 - accuracy: 0.3859 - val_loss: 1.1868 - val_accuracy: 0.3652 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.3933 - val_loss: 1.1944 - val_accuracy: 0.3652 Epoch 68/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.3880 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.4217 - val_loss: 1.1907 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3996 - val_loss: 1.1670 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.3880 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 72/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.3817 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.4122 - val_loss: 1.1805 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.3817 - val_loss: 1.1897 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2285 - accuracy: 0.3922 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2279 - accuracy: 0.3933 - val_loss: 1.1822 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4101 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3985 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4048 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3775 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 81/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2267 - accuracy: 0.3954 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2280 - accuracy: 0.4006 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.4101 - val_loss: 1.1684 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4006 - val_loss: 1.1807 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3849 - val_loss: 1.1877 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3996 - val_loss: 1.1708 - val_accuracy: 0.4706 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4269 - val_loss: 1.1771 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3849 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2253 - accuracy: 0.4143 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3859 - val_loss: 1.1654 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2273 - accuracy: 0.4048 - val_loss: 1.1932 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3943 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4059 - val_loss: 1.1742 - val_accuracy: 0.3652 Epoch 94/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.4122 - val_loss: 1.1870 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2285 - accuracy: 0.3575 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.4217 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.4017 - val_loss: 1.1644 - val_accuracy: 0.4706 Epoch 98/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.3828 - val_loss: 1.1882 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2258 - accuracy: 0.4154 - val_loss: 1.1721 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2263 - accuracy: 0.4132 - val_loss: 1.1652 - val_accuracy: 0.4706 13/13 [==============================] - 0s 5ms/step Epoch 1/100 96/96 [==============================] - 3s 12ms/step - loss: 1.2272 - accuracy: 0.4069 - val_loss: 1.1781 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.4111 - val_loss: 1.1885 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2289 - accuracy: 0.3922 - val_loss: 1.1698 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4006 - val_loss: 1.1984 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4175 - val_loss: 1.1788 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4090 - val_loss: 1.1672 - val_accuracy: 0.4706 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3859 - val_loss: 1.1665 - val_accuracy: 0.4706 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2287 - accuracy: 0.3880 - val_loss: 1.1848 - val_accuracy: 0.3652 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2254 - accuracy: 0.4227 - val_loss: 1.1896 - val_accuracy: 0.3652 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.4164 - val_loss: 1.1853 - val_accuracy: 0.4706 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4154 - val_loss: 1.1939 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4027 - val_loss: 1.1922 - val_accuracy: 0.3652 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2245 - accuracy: 0.4343 - val_loss: 1.1834 - val_accuracy: 0.3652 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4132 - val_loss: 1.1739 - val_accuracy: 0.3652 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.3996 - val_loss: 1.1769 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3964 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3933 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3964 - val_loss: 1.1682 - val_accuracy: 0.4706 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2282 - accuracy: 0.3817 - val_loss: 1.1904 - val_accuracy: 0.3652 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3922 - val_loss: 1.1756 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2288 - accuracy: 0.3964 - val_loss: 1.1766 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2269 - accuracy: 0.3901 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2259 - accuracy: 0.4017 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4038 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2292 - accuracy: 0.3912 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 26/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.4059 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.3922 - val_loss: 1.1645 - val_accuracy: 0.4706 Epoch 28/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2279 - accuracy: 0.3891 - val_loss: 1.1962 - val_accuracy: 0.3652 Epoch 29/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.4238 - val_loss: 1.1754 - val_accuracy: 0.3652 Epoch 30/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2260 - accuracy: 0.4006 - val_loss: 1.1761 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.4048 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.3943 - val_loss: 1.1731 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.4006 - val_loss: 1.1742 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3996 - val_loss: 1.1782 - val_accuracy: 0.4706 Epoch 35/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.4101 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.3943 - val_loss: 1.1663 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2227 - accuracy: 0.4427 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 38/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2260 - accuracy: 0.4154 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.3964 - val_loss: 1.1746 - val_accuracy: 0.3652 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2249 - accuracy: 0.4164 - val_loss: 1.1713 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3722 - val_loss: 1.1955 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.4111 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2263 - accuracy: 0.4048 - val_loss: 1.1736 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.4069 - val_loss: 1.1661 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2245 - accuracy: 0.4175 - val_loss: 1.2058 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.4132 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 47/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.4122 - val_loss: 1.1736 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4132 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 49/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.4185 - val_loss: 1.1923 - val_accuracy: 0.3652 Epoch 50/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2285 - accuracy: 0.3985 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 51/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.4006 - val_loss: 1.1906 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2247 - accuracy: 0.4269 - val_loss: 1.1646 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.3870 - val_loss: 1.1706 - val_accuracy: 0.4706 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2274 - accuracy: 0.3933 - val_loss: 1.1824 - val_accuracy: 0.3652 Epoch 55/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.3754 - val_loss: 1.1808 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2268 - accuracy: 0.3870 - val_loss: 1.1893 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2282 - accuracy: 0.3933 - val_loss: 1.1952 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2291 - accuracy: 0.3901 - val_loss: 1.1677 - val_accuracy: 0.4706 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4006 - val_loss: 1.1648 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3922 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3775 - val_loss: 1.1764 - val_accuracy: 0.3652 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3901 - val_loss: 1.1953 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4006 - val_loss: 1.1864 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4059 - val_loss: 1.1711 - val_accuracy: 0.4706 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3733 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.3996 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3849 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.3996 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2271 - accuracy: 0.4111 - val_loss: 1.1861 - val_accuracy: 0.3652 Epoch 70/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2264 - accuracy: 0.4059 - val_loss: 1.1717 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2270 - accuracy: 0.3933 - val_loss: 1.1921 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.4017 - val_loss: 1.1655 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2277 - accuracy: 0.4059 - val_loss: 1.1744 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2241 - accuracy: 0.4206 - val_loss: 1.1727 - val_accuracy: 0.4706 Epoch 75/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.4038 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2266 - accuracy: 0.4185 - val_loss: 1.1666 - val_accuracy: 0.4706 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2270 - accuracy: 0.3975 - val_loss: 1.1966 - val_accuracy: 0.3652 Epoch 78/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3901 - val_loss: 1.1818 - val_accuracy: 0.3652 Epoch 79/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2271 - accuracy: 0.3870 - val_loss: 1.1898 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2277 - accuracy: 0.3817 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.3943 - val_loss: 1.2016 - val_accuracy: 0.3652 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3849 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.3807 - val_loss: 1.1719 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2272 - accuracy: 0.4006 - val_loss: 1.1774 - val_accuracy: 0.3652 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3901 - val_loss: 1.1885 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2256 - accuracy: 0.4132 - val_loss: 1.1827 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3954 - val_loss: 1.1718 - val_accuracy: 0.3652 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.4038 - val_loss: 1.1789 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.4090 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.3933 - val_loss: 1.1671 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4122 - val_loss: 1.1957 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3975 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2261 - accuracy: 0.4227 - val_loss: 1.1731 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.3954 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4069 - val_loss: 1.1681 - val_accuracy: 0.4706 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3775 - val_loss: 1.1688 - val_accuracy: 0.4706 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4069 - val_loss: 1.1885 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3996 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2262 - accuracy: 0.4059 - val_loss: 1.1971 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4090 - val_loss: 1.1745 - val_accuracy: 0.4706 13/13 [==============================] - 0s 5ms/step Epoch 1/100 96/96 [==============================] - 3s 13ms/step - loss: 1.2272 - accuracy: 0.4069 - val_loss: 1.1931 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.4017 - val_loss: 1.1680 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2275 - accuracy: 0.3985 - val_loss: 1.1774 - val_accuracy: 0.4706 Epoch 4/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2282 - accuracy: 0.3891 - val_loss: 1.1786 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.4164 - val_loss: 1.1817 - val_accuracy: 0.3652 Epoch 6/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.4038 - val_loss: 1.1758 - val_accuracy: 0.3652 Epoch 7/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2233 - accuracy: 0.4353 - val_loss: 1.2053 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2278 - accuracy: 0.4069 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 9/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.4027 - val_loss: 1.1676 - val_accuracy: 0.4706 Epoch 10/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4006 - val_loss: 1.1800 - val_accuracy: 0.3652 Epoch 11/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2267 - accuracy: 0.3828 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3996 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4080 - val_loss: 1.1640 - val_accuracy: 0.4706 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4101 - val_loss: 1.1643 - val_accuracy: 0.4706 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2271 - accuracy: 0.4069 - val_loss: 1.1860 - val_accuracy: 0.3652 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3933 - val_loss: 1.1662 - val_accuracy: 0.4706 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2267 - accuracy: 0.4206 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.4080 - val_loss: 1.1832 - val_accuracy: 0.3652 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2265 - accuracy: 0.4154 - val_loss: 1.1679 - val_accuracy: 0.4706 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2246 - accuracy: 0.4122 - val_loss: 1.1736 - val_accuracy: 0.4706 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3985 - val_loss: 1.1992 - val_accuracy: 0.3652 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4059 - val_loss: 1.1686 - val_accuracy: 0.4706 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.4196 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.3922 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.4048 - val_loss: 1.1844 - val_accuracy: 0.3652 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2283 - accuracy: 0.3880 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4227 - val_loss: 1.1869 - val_accuracy: 0.3652 Epoch 28/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2252 - accuracy: 0.4322 - val_loss: 1.1728 - val_accuracy: 0.4706 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2286 - accuracy: 0.4048 - val_loss: 1.1675 - val_accuracy: 0.4706 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3838 - val_loss: 1.1768 - val_accuracy: 0.3652 Epoch 31/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2274 - accuracy: 0.4164 - val_loss: 1.1657 - val_accuracy: 0.4706 Epoch 32/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2270 - accuracy: 0.3828 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 33/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3954 - val_loss: 1.1846 - val_accuracy: 0.4706 Epoch 34/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2286 - accuracy: 0.3922 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 35/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.3912 - val_loss: 1.1785 - val_accuracy: 0.3652 Epoch 36/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.4027 - val_loss: 1.1649 - val_accuracy: 0.4706 Epoch 37/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.4017 - val_loss: 1.1871 - val_accuracy: 0.3652 Epoch 38/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2261 - accuracy: 0.4038 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 39/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2253 - accuracy: 0.4248 - val_loss: 1.1659 - val_accuracy: 0.4706 Epoch 40/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.3880 - val_loss: 1.1757 - val_accuracy: 0.3652 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.3985 - val_loss: 1.1835 - val_accuracy: 0.3652 Epoch 42/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2272 - accuracy: 0.3743 - val_loss: 1.1683 - val_accuracy: 0.4706 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.4111 - val_loss: 1.1745 - val_accuracy: 0.4706 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2284 - accuracy: 0.3828 - val_loss: 1.1650 - val_accuracy: 0.4706 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3870 - val_loss: 1.1793 - val_accuracy: 0.3652 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4101 - val_loss: 1.1667 - val_accuracy: 0.4706 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3933 - val_loss: 1.1884 - val_accuracy: 0.3652 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2280 - accuracy: 0.3954 - val_loss: 1.1796 - val_accuracy: 0.3652 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4196 - val_loss: 1.1709 - val_accuracy: 0.4706 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4101 - val_loss: 1.1873 - val_accuracy: 0.3652 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.4017 - val_loss: 1.1821 - val_accuracy: 0.3652 Epoch 52/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.4080 - val_loss: 1.1705 - val_accuracy: 0.4706 Epoch 53/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.3817 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.3985 - val_loss: 1.1725 - val_accuracy: 0.4706 Epoch 55/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2257 - accuracy: 0.3996 - val_loss: 1.1762 - val_accuracy: 0.3652 Epoch 56/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3912 - val_loss: 1.1760 - val_accuracy: 0.3652 Epoch 57/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2276 - accuracy: 0.3817 - val_loss: 1.1929 - val_accuracy: 0.3652 Epoch 58/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2272 - accuracy: 0.3817 - val_loss: 1.1846 - val_accuracy: 0.3652 Epoch 59/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2265 - accuracy: 0.4269 - val_loss: 1.1736 - val_accuracy: 0.4706 Epoch 60/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2280 - accuracy: 0.3701 - val_loss: 1.1645 - val_accuracy: 0.4706 Epoch 61/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2273 - accuracy: 0.3743 - val_loss: 1.1646 - val_accuracy: 0.4706 Epoch 62/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2278 - accuracy: 0.3912 - val_loss: 1.1816 - val_accuracy: 0.3652 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2266 - accuracy: 0.4038 - val_loss: 1.1866 - val_accuracy: 0.3652 Epoch 64/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2262 - accuracy: 0.4101 - val_loss: 1.1658 - val_accuracy: 0.4706 Epoch 65/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2263 - accuracy: 0.4132 - val_loss: 1.1898 - val_accuracy: 0.3652 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3954 - val_loss: 1.1813 - val_accuracy: 0.4706 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2260 - accuracy: 0.4006 - val_loss: 1.1700 - val_accuracy: 0.4706 Epoch 68/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2261 - accuracy: 0.4069 - val_loss: 1.1779 - val_accuracy: 0.4706 Epoch 69/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2269 - accuracy: 0.3954 - val_loss: 1.1808 - val_accuracy: 0.4706 Epoch 70/100 96/96 [==============================] - 1s 10ms/step - loss: 1.2279 - accuracy: 0.3870 - val_loss: 1.1678 - val_accuracy: 0.4706 Epoch 71/100 96/96 [==============================] - 1s 8ms/step - loss: 1.2267 - accuracy: 0.3764 - val_loss: 1.1893 - val_accuracy: 0.3652 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2276 - accuracy: 0.4101 - val_loss: 1.1647 - val_accuracy: 0.4706 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2256 - accuracy: 0.3996 - val_loss: 1.2065 - val_accuracy: 0.3652 Epoch 74/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2275 - accuracy: 0.4122 - val_loss: 1.1877 - val_accuracy: 0.3652 Epoch 75/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2273 - accuracy: 0.4027 - val_loss: 1.1674 - val_accuracy: 0.4706 Epoch 76/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2274 - accuracy: 0.3838 - val_loss: 1.1737 - val_accuracy: 0.3652 Epoch 77/100 96/96 [==============================] - 1s 7ms/step - loss: 1.2232 - accuracy: 0.4290 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2277 - accuracy: 0.3859 - val_loss: 1.1793 - val_accuracy: 0.4706 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2200 - accuracy: 0.4564 - val_loss: 1.2533 - val_accuracy: 0.3652 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2307 - accuracy: 0.3975 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2273 - accuracy: 0.3912 - val_loss: 1.1673 - val_accuracy: 0.4706 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3901 - val_loss: 1.1660 - val_accuracy: 0.4706 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2257 - accuracy: 0.3996 - val_loss: 1.1696 - val_accuracy: 0.4706 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2278 - accuracy: 0.3743 - val_loss: 1.1685 - val_accuracy: 0.4706 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3754 - val_loss: 1.2011 - val_accuracy: 0.3652 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4122 - val_loss: 1.1901 - val_accuracy: 0.3652 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.3996 - val_loss: 1.1826 - val_accuracy: 0.4706 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.4101 - val_loss: 1.2010 - val_accuracy: 0.3652 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2255 - accuracy: 0.4227 - val_loss: 1.1814 - val_accuracy: 0.3652 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2279 - accuracy: 0.3954 - val_loss: 1.1664 - val_accuracy: 0.4706 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2252 - accuracy: 0.4132 - val_loss: 1.2012 - val_accuracy: 0.3652 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2269 - accuracy: 0.4132 - val_loss: 1.1656 - val_accuracy: 0.4706 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3828 - val_loss: 1.1715 - val_accuracy: 0.4706 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2264 - accuracy: 0.3954 - val_loss: 1.1847 - val_accuracy: 0.3652 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2275 - accuracy: 0.3964 - val_loss: 1.1878 - val_accuracy: 0.3652 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2268 - accuracy: 0.3922 - val_loss: 1.1787 - val_accuracy: 0.3652 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2266 - accuracy: 0.3943 - val_loss: 1.1770 - val_accuracy: 0.3652 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2281 - accuracy: 0.3838 - val_loss: 1.1807 - val_accuracy: 0.3652 Epoch 99/100 96/96 [==============================] - 1s 5ms/step - loss: 1.2264 - accuracy: 0.3996 - val_loss: 1.1895 - val_accuracy: 0.3652 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2270 - accuracy: 0.4090 - val_loss: 1.1877 - val_accuracy: 0.3652 13/13 [==============================] - 0s 4ms/step
print(RESULTS)
LEARNING RATE MODEL SCORE 0 0.01 -0.002490 1 0.02 -0.002477 2 0.03 -0.002248 3 0.04 -0.003932 4 0.05 -0.001524 5 0.06 -0.005578 6 0.07 -0.004509 7 0.08 -0.001374 8 0.09 -0.005196 9 0.10 -0.009711
* MODEL SCORE IS NOT IMPROVED WITH THE DIFFERENT LEARNING RATES.
# USE SIGMOID FUNCTION INSTEAD OF RELU AND COMPARE THE RESULTS
NN_MODEL_1 = Sequential()
NN_MODEL_1.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='sigmoid'))
NN_MODEL_1.add(tf.keras.layers.BatchNormalization())
NN_MODEL_1.add(Dense(128, kernel_initializer='normal', activation='sigmoid'))
NN_MODEL_1.add(Dense(128, kernel_initializer='normal'))
NN_MODEL_1.add(LeakyReLU(alpha=0.1))
NN_MODEL_1.add(Dense(9, activation ="softmax"))
NN_MODEL_1.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY_SIGMOID = NN_MODEL_1.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 2s 8ms/step - loss: 1.3927 - accuracy: 0.4017 - val_loss: 1.2364 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 5ms/step - loss: 1.1889 - accuracy: 0.5079 - val_loss: 1.1702 - val_accuracy: 0.4706 Epoch 3/100 96/96 [==============================] - 0s 5ms/step - loss: 1.1211 - accuracy: 0.5373 - val_loss: 1.2393 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 5ms/step - loss: 1.0806 - accuracy: 0.5394 - val_loss: 1.1174 - val_accuracy: 0.4755 Epoch 5/100 96/96 [==============================] - 0s 5ms/step - loss: 1.0587 - accuracy: 0.5605 - val_loss: 1.0298 - val_accuracy: 0.5392 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0599 - accuracy: 0.5573 - val_loss: 1.0226 - val_accuracy: 0.5319 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0467 - accuracy: 0.5468 - val_loss: 0.9840 - val_accuracy: 0.6005 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0179 - accuracy: 0.5931 - val_loss: 0.9884 - val_accuracy: 0.5809 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0265 - accuracy: 0.5783 - val_loss: 0.9512 - val_accuracy: 0.6127 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0071 - accuracy: 0.5857 - val_loss: 0.9474 - val_accuracy: 0.6103 Epoch 11/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0097 - accuracy: 0.5752 - val_loss: 1.0286 - val_accuracy: 0.5466 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0032 - accuracy: 0.5731 - val_loss: 0.9837 - val_accuracy: 0.5980 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0124 - accuracy: 0.5605 - val_loss: 0.9544 - val_accuracy: 0.6078 Epoch 14/100 96/96 [==============================] - 1s 5ms/step - loss: 1.0013 - accuracy: 0.5678 - val_loss: 1.0050 - val_accuracy: 0.5637 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9911 - accuracy: 0.5846 - val_loss: 1.0624 - val_accuracy: 0.4755 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0087 - accuracy: 0.5626 - val_loss: 0.9890 - val_accuracy: 0.5833 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9985 - accuracy: 0.5762 - val_loss: 0.9685 - val_accuracy: 0.5956 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0044 - accuracy: 0.5741 - val_loss: 0.9701 - val_accuracy: 0.5882 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0047 - accuracy: 0.5552 - val_loss: 0.9890 - val_accuracy: 0.5490 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9938 - accuracy: 0.5699 - val_loss: 0.9589 - val_accuracy: 0.6103 Epoch 21/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9859 - accuracy: 0.5952 - val_loss: 0.9756 - val_accuracy: 0.5931 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9924 - accuracy: 0.5699 - val_loss: 1.0081 - val_accuracy: 0.5343 Epoch 23/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9736 - accuracy: 0.5647 - val_loss: 0.9642 - val_accuracy: 0.6029 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9736 - accuracy: 0.5815 - val_loss: 1.0578 - val_accuracy: 0.5294 Epoch 25/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9844 - accuracy: 0.5836 - val_loss: 0.9923 - val_accuracy: 0.5735 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9834 - accuracy: 0.5878 - val_loss: 0.9684 - val_accuracy: 0.5882 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9741 - accuracy: 0.5773 - val_loss: 0.9517 - val_accuracy: 0.5882 Epoch 28/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9723 - accuracy: 0.5773 - val_loss: 1.0205 - val_accuracy: 0.5343 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9644 - accuracy: 0.5710 - val_loss: 0.9584 - val_accuracy: 0.5956 Epoch 30/100 96/96 [==============================] - 0s 5ms/step - loss: 0.9736 - accuracy: 0.5825 - val_loss: 1.0158 - val_accuracy: 0.5343 Epoch 31/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9782 - accuracy: 0.5825 - val_loss: 0.9983 - val_accuracy: 0.5686 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9693 - accuracy: 0.5920 - val_loss: 0.9483 - val_accuracy: 0.5809 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9646 - accuracy: 0.5678 - val_loss: 0.9709 - val_accuracy: 0.5882 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9669 - accuracy: 0.5815 - val_loss: 1.0457 - val_accuracy: 0.5025 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9724 - accuracy: 0.5857 - val_loss: 1.0260 - val_accuracy: 0.5270 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9700 - accuracy: 0.5846 - val_loss: 1.0094 - val_accuracy: 0.5735 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9788 - accuracy: 0.5689 - val_loss: 0.9485 - val_accuracy: 0.6005 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9817 - accuracy: 0.5720 - val_loss: 0.9785 - val_accuracy: 0.5686 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9758 - accuracy: 0.5636 - val_loss: 1.0144 - val_accuracy: 0.5343 Epoch 40/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9758 - accuracy: 0.5657 - val_loss: 0.9628 - val_accuracy: 0.5931 Epoch 41/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9622 - accuracy: 0.5941 - val_loss: 1.0534 - val_accuracy: 0.5221 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9684 - accuracy: 0.5941 - val_loss: 0.9675 - val_accuracy: 0.5980 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9515 - accuracy: 0.5920 - val_loss: 0.9669 - val_accuracy: 0.5833 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9604 - accuracy: 0.5752 - val_loss: 1.0175 - val_accuracy: 0.5196 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9604 - accuracy: 0.5741 - val_loss: 0.9644 - val_accuracy: 0.5858 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9629 - accuracy: 0.5920 - val_loss: 0.9547 - val_accuracy: 0.5931 Epoch 47/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9600 - accuracy: 0.5899 - val_loss: 1.0626 - val_accuracy: 0.5270 Epoch 48/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9546 - accuracy: 0.5741 - val_loss: 0.9719 - val_accuracy: 0.5784 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9477 - accuracy: 0.5783 - val_loss: 0.9598 - val_accuracy: 0.5882 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9488 - accuracy: 0.5931 - val_loss: 0.9576 - val_accuracy: 0.5809 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9478 - accuracy: 0.5710 - val_loss: 0.9670 - val_accuracy: 0.5907 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9471 - accuracy: 0.5762 - val_loss: 0.9648 - val_accuracy: 0.5784 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9515 - accuracy: 0.5941 - val_loss: 0.9805 - val_accuracy: 0.5809 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9499 - accuracy: 0.5762 - val_loss: 1.0030 - val_accuracy: 0.5539 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9487 - accuracy: 0.5962 - val_loss: 1.0137 - val_accuracy: 0.5319 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9582 - accuracy: 0.5521 - val_loss: 0.9935 - val_accuracy: 0.5662 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9476 - accuracy: 0.5931 - val_loss: 0.9750 - val_accuracy: 0.5784 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9401 - accuracy: 0.5994 - val_loss: 0.9536 - val_accuracy: 0.5931 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9421 - accuracy: 0.6015 - val_loss: 0.9758 - val_accuracy: 0.5931 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9388 - accuracy: 0.5846 - val_loss: 1.0213 - val_accuracy: 0.5294 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9362 - accuracy: 0.5836 - val_loss: 0.9945 - val_accuracy: 0.5588 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9447 - accuracy: 0.5815 - val_loss: 0.9563 - val_accuracy: 0.5760 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9438 - accuracy: 0.5868 - val_loss: 0.9713 - val_accuracy: 0.5784 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9275 - accuracy: 0.5962 - val_loss: 0.9874 - val_accuracy: 0.5735 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9476 - accuracy: 0.5773 - val_loss: 0.9855 - val_accuracy: 0.5784 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9296 - accuracy: 0.5941 - val_loss: 0.9811 - val_accuracy: 0.5882 Epoch 67/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9622 - accuracy: 0.5878 - val_loss: 1.0081 - val_accuracy: 0.5466 Epoch 68/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9488 - accuracy: 0.5794 - val_loss: 0.9951 - val_accuracy: 0.5637 Epoch 69/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9385 - accuracy: 0.5857 - val_loss: 1.0191 - val_accuracy: 0.5613 Epoch 70/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9499 - accuracy: 0.5920 - val_loss: 0.9940 - val_accuracy: 0.5539 Epoch 71/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9473 - accuracy: 0.5794 - val_loss: 0.9918 - val_accuracy: 0.5637 Epoch 72/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9441 - accuracy: 0.6078 - val_loss: 0.9481 - val_accuracy: 0.5956 Epoch 73/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9328 - accuracy: 0.5783 - val_loss: 0.9697 - val_accuracy: 0.5833 Epoch 74/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9361 - accuracy: 0.5973 - val_loss: 0.9541 - val_accuracy: 0.5907 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9401 - accuracy: 0.6193 - val_loss: 0.9766 - val_accuracy: 0.5784 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9338 - accuracy: 0.5962 - val_loss: 0.9500 - val_accuracy: 0.5907 Epoch 77/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9404 - accuracy: 0.5994 - val_loss: 0.9829 - val_accuracy: 0.5882 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9404 - accuracy: 0.6109 - val_loss: 0.9982 - val_accuracy: 0.5711 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9213 - accuracy: 0.6046 - val_loss: 0.9493 - val_accuracy: 0.5931 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9357 - accuracy: 0.5657 - val_loss: 1.0760 - val_accuracy: 0.4828 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9305 - accuracy: 0.6015 - val_loss: 0.9576 - val_accuracy: 0.5833 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9258 - accuracy: 0.6004 - val_loss: 1.0209 - val_accuracy: 0.5441 Epoch 83/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9387 - accuracy: 0.5983 - val_loss: 0.9607 - val_accuracy: 0.5686 Epoch 84/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9265 - accuracy: 0.6036 - val_loss: 0.9546 - val_accuracy: 0.5907 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9367 - accuracy: 0.6036 - val_loss: 0.9969 - val_accuracy: 0.5662 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9102 - accuracy: 0.6141 - val_loss: 0.9602 - val_accuracy: 0.5956 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9391 - accuracy: 0.5899 - val_loss: 0.9541 - val_accuracy: 0.5882 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9286 - accuracy: 0.6046 - val_loss: 1.0156 - val_accuracy: 0.5539 Epoch 89/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9337 - accuracy: 0.5983 - val_loss: 1.0194 - val_accuracy: 0.5613 Epoch 90/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9164 - accuracy: 0.5910 - val_loss: 0.9461 - val_accuracy: 0.5931 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9348 - accuracy: 0.5941 - val_loss: 0.9544 - val_accuracy: 0.5980 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9183 - accuracy: 0.5983 - val_loss: 0.9508 - val_accuracy: 0.6029 Epoch 93/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9399 - accuracy: 0.5952 - val_loss: 0.9888 - val_accuracy: 0.5662 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9239 - accuracy: 0.6004 - val_loss: 0.9507 - val_accuracy: 0.5858 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9225 - accuracy: 0.6099 - val_loss: 0.9603 - val_accuracy: 0.5858 Epoch 96/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9110 - accuracy: 0.5910 - val_loss: 0.9578 - val_accuracy: 0.5760 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9072 - accuracy: 0.5983 - val_loss: 0.9982 - val_accuracy: 0.5686 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9112 - accuracy: 0.6078 - val_loss: 1.0162 - val_accuracy: 0.5539 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9209 - accuracy: 0.6067 - val_loss: 0.9515 - val_accuracy: 0.5956 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9088 - accuracy: 0.6025 - val_loss: 0.9499 - val_accuracy: 0.5980
HIST_SIGDF = pd.DataFrame.from_dict(HISTORY_SIGMOID.history)
y_pred = NN_MODEL_1.predict(x_test_s)
print('SCORE:',metrics.r2_score(y_test_c,y_pred))
print('\n\n',HIST_SIGDF.sort_values('val_accuracy',ascending = False).head())
13/13 [==============================] - 0s 3ms/step
SCORE: 0.07041858026786495
loss accuracy val_loss val_accuracy
8 1.026517 0.578339 0.951217 0.612745
19 0.993754 0.569926 0.958910 0.610294
9 1.007084 0.585699 0.947364 0.610294
12 1.012399 0.560463 0.954428 0.607843
91 0.918346 0.598318 0.950822 0.602941
# USE TANH FUNCTION INSTEAD OF RELU AND COMPARE THE RESULTS
NN_MODEL_2 = Sequential()
NN_MODEL_2.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='tanh'))
NN_MODEL_2.add(tf.keras.layers.BatchNormalization())
NN_MODEL_2.add(Dense(128, kernel_initializer='normal', activation='tanh'))
NN_MODEL_2.add(Dense(128, kernel_initializer='normal'))
NN_MODEL_2.add(LeakyReLU(alpha=0.1))
NN_MODEL_2.add(Dense(9, activation ="softmax"))
NN_MODEL_2.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY_TANH = NN_MODEL_2.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 3s 9ms/step - loss: 1.3597 - accuracy: 0.4921 - val_loss: 1.6315 - val_accuracy: 0.6054 Epoch 2/100 96/96 [==============================] - 1s 6ms/step - loss: 1.1017 - accuracy: 0.5626 - val_loss: 1.2690 - val_accuracy: 0.5956 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0347 - accuracy: 0.5804 - val_loss: 1.0877 - val_accuracy: 0.5613 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0121 - accuracy: 0.5910 - val_loss: 1.0393 - val_accuracy: 0.5662 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9867 - accuracy: 0.5825 - val_loss: 1.0092 - val_accuracy: 0.5931 Epoch 6/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9982 - accuracy: 0.5741 - val_loss: 0.9767 - val_accuracy: 0.5858 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9579 - accuracy: 0.5941 - val_loss: 0.9736 - val_accuracy: 0.6152 Epoch 8/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9486 - accuracy: 0.6204 - val_loss: 0.9902 - val_accuracy: 0.5833 Epoch 9/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9533 - accuracy: 0.5773 - val_loss: 0.9851 - val_accuracy: 0.5907 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9200 - accuracy: 0.6172 - val_loss: 0.9666 - val_accuracy: 0.6152 Epoch 11/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9305 - accuracy: 0.6120 - val_loss: 0.9983 - val_accuracy: 0.5931 Epoch 12/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9312 - accuracy: 0.6036 - val_loss: 1.0144 - val_accuracy: 0.5515 Epoch 13/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9123 - accuracy: 0.6130 - val_loss: 0.9805 - val_accuracy: 0.5882 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8904 - accuracy: 0.6193 - val_loss: 0.9932 - val_accuracy: 0.5907 Epoch 15/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9003 - accuracy: 0.5994 - val_loss: 0.9824 - val_accuracy: 0.5882 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9282 - accuracy: 0.6151 - val_loss: 1.0019 - val_accuracy: 0.5760 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8824 - accuracy: 0.6109 - val_loss: 0.9932 - val_accuracy: 0.5907 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9079 - accuracy: 0.6120 - val_loss: 1.0022 - val_accuracy: 0.5980 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8919 - accuracy: 0.6130 - val_loss: 1.0178 - val_accuracy: 0.5686 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8866 - accuracy: 0.6278 - val_loss: 1.0243 - val_accuracy: 0.5441 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8615 - accuracy: 0.6362 - val_loss: 0.9839 - val_accuracy: 0.5931 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8746 - accuracy: 0.6341 - val_loss: 0.9831 - val_accuracy: 0.5907 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8633 - accuracy: 0.6246 - val_loss: 0.9891 - val_accuracy: 0.6005 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8613 - accuracy: 0.6278 - val_loss: 1.0031 - val_accuracy: 0.5980 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8392 - accuracy: 0.6467 - val_loss: 0.9964 - val_accuracy: 0.6054 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8731 - accuracy: 0.6225 - val_loss: 1.0030 - val_accuracy: 0.5858 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8444 - accuracy: 0.6477 - val_loss: 1.0368 - val_accuracy: 0.5613 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8233 - accuracy: 0.6551 - val_loss: 1.0039 - val_accuracy: 0.5907 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8309 - accuracy: 0.6456 - val_loss: 1.0061 - val_accuracy: 0.5907 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8327 - accuracy: 0.6477 - val_loss: 1.0176 - val_accuracy: 0.6103 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8192 - accuracy: 0.6635 - val_loss: 1.0217 - val_accuracy: 0.5931 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8362 - accuracy: 0.6456 - val_loss: 0.9980 - val_accuracy: 0.5907 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8432 - accuracy: 0.6425 - val_loss: 1.0233 - val_accuracy: 0.5760 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8246 - accuracy: 0.6435 - val_loss: 1.0329 - val_accuracy: 0.6005 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8499 - accuracy: 0.6446 - val_loss: 1.0441 - val_accuracy: 0.5539 Epoch 36/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8197 - accuracy: 0.6362 - val_loss: 1.0288 - val_accuracy: 0.5613 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8094 - accuracy: 0.6604 - val_loss: 1.0116 - val_accuracy: 0.5735 Epoch 38/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8172 - accuracy: 0.6351 - val_loss: 0.9882 - val_accuracy: 0.6103 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8140 - accuracy: 0.6604 - val_loss: 1.0182 - val_accuracy: 0.5760 Epoch 40/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8032 - accuracy: 0.6477 - val_loss: 1.0111 - val_accuracy: 0.5711 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8089 - accuracy: 0.6383 - val_loss: 1.0026 - val_accuracy: 0.5809 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8128 - accuracy: 0.6383 - val_loss: 1.0160 - val_accuracy: 0.5980 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7984 - accuracy: 0.6614 - val_loss: 1.0159 - val_accuracy: 0.5858 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7942 - accuracy: 0.6835 - val_loss: 1.0225 - val_accuracy: 0.5686 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8015 - accuracy: 0.6519 - val_loss: 1.0240 - val_accuracy: 0.5956 Epoch 46/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8056 - accuracy: 0.6519 - val_loss: 1.0354 - val_accuracy: 0.5613 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8157 - accuracy: 0.6562 - val_loss: 1.0155 - val_accuracy: 0.5833 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8134 - accuracy: 0.6383 - val_loss: 1.0464 - val_accuracy: 0.5613 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8093 - accuracy: 0.6540 - val_loss: 1.0337 - val_accuracy: 0.5637 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7720 - accuracy: 0.6698 - val_loss: 1.0436 - val_accuracy: 0.5588 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7538 - accuracy: 0.6772 - val_loss: 1.0746 - val_accuracy: 0.5637 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7791 - accuracy: 0.6562 - val_loss: 1.0517 - val_accuracy: 0.5980 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7559 - accuracy: 0.6814 - val_loss: 1.0614 - val_accuracy: 0.5613 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7731 - accuracy: 0.6730 - val_loss: 1.0512 - val_accuracy: 0.5735 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7886 - accuracy: 0.6877 - val_loss: 1.0738 - val_accuracy: 0.5711 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7473 - accuracy: 0.6866 - val_loss: 1.0736 - val_accuracy: 0.5711 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7876 - accuracy: 0.6614 - val_loss: 1.0553 - val_accuracy: 0.5882 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7688 - accuracy: 0.6635 - val_loss: 1.0517 - val_accuracy: 0.5809 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7391 - accuracy: 0.6993 - val_loss: 1.1194 - val_accuracy: 0.5515 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7529 - accuracy: 0.6877 - val_loss: 1.0670 - val_accuracy: 0.5539 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7638 - accuracy: 0.6824 - val_loss: 1.0885 - val_accuracy: 0.5760 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7524 - accuracy: 0.6667 - val_loss: 1.0965 - val_accuracy: 0.5490 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7687 - accuracy: 0.6887 - val_loss: 1.1044 - val_accuracy: 0.5588 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7630 - accuracy: 0.6698 - val_loss: 1.0732 - val_accuracy: 0.5882 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7452 - accuracy: 0.6856 - val_loss: 1.1133 - val_accuracy: 0.5319 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7477 - accuracy: 0.6877 - val_loss: 1.1002 - val_accuracy: 0.5686 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7431 - accuracy: 0.6961 - val_loss: 1.0672 - val_accuracy: 0.5760 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7569 - accuracy: 0.6772 - val_loss: 1.0858 - val_accuracy: 0.5637 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7395 - accuracy: 0.6845 - val_loss: 1.1001 - val_accuracy: 0.5637 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7356 - accuracy: 0.7024 - val_loss: 1.0831 - val_accuracy: 0.6054 Epoch 71/100 96/96 [==============================] - 1s 7ms/step - loss: 0.7448 - accuracy: 0.6677 - val_loss: 1.0918 - val_accuracy: 0.5907 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7546 - accuracy: 0.6824 - val_loss: 1.0731 - val_accuracy: 0.5490 Epoch 73/100 96/96 [==============================] - 1s 7ms/step - loss: 0.7231 - accuracy: 0.6951 - val_loss: 1.0849 - val_accuracy: 0.5466 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7238 - accuracy: 0.7035 - val_loss: 1.1453 - val_accuracy: 0.5196 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7254 - accuracy: 0.7003 - val_loss: 1.1303 - val_accuracy: 0.5637 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7185 - accuracy: 0.6909 - val_loss: 1.1368 - val_accuracy: 0.5343 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7184 - accuracy: 0.7035 - val_loss: 1.1109 - val_accuracy: 0.5417 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7379 - accuracy: 0.6909 - val_loss: 1.0888 - val_accuracy: 0.5784 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7092 - accuracy: 0.7182 - val_loss: 1.1048 - val_accuracy: 0.5343 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6895 - accuracy: 0.7077 - val_loss: 1.1126 - val_accuracy: 0.5809 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7396 - accuracy: 0.6940 - val_loss: 1.0997 - val_accuracy: 0.5466 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7162 - accuracy: 0.6887 - val_loss: 1.0906 - val_accuracy: 0.5588 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6836 - accuracy: 0.7035 - val_loss: 1.1430 - val_accuracy: 0.5588 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7222 - accuracy: 0.7087 - val_loss: 1.1404 - val_accuracy: 0.5564 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7258 - accuracy: 0.6909 - val_loss: 1.1604 - val_accuracy: 0.5270 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7044 - accuracy: 0.7140 - val_loss: 1.1584 - val_accuracy: 0.5343 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6911 - accuracy: 0.7066 - val_loss: 1.1519 - val_accuracy: 0.5490 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7208 - accuracy: 0.6930 - val_loss: 1.1192 - val_accuracy: 0.5588 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7085 - accuracy: 0.7003 - val_loss: 1.1368 - val_accuracy: 0.5588 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6900 - accuracy: 0.7056 - val_loss: 1.1594 - val_accuracy: 0.5343 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6922 - accuracy: 0.7140 - val_loss: 1.1625 - val_accuracy: 0.5735 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7053 - accuracy: 0.7087 - val_loss: 1.1716 - val_accuracy: 0.5539 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6815 - accuracy: 0.7308 - val_loss: 1.1829 - val_accuracy: 0.5417 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7199 - accuracy: 0.6993 - val_loss: 1.1110 - val_accuracy: 0.5735 Epoch 95/100 96/96 [==============================] - 1s 9ms/step - loss: 0.7027 - accuracy: 0.6982 - val_loss: 1.1955 - val_accuracy: 0.5392 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6776 - accuracy: 0.7245 - val_loss: 1.1662 - val_accuracy: 0.5490 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6729 - accuracy: 0.7140 - val_loss: 1.1324 - val_accuracy: 0.5588 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6897 - accuracy: 0.7224 - val_loss: 1.1202 - val_accuracy: 0.5613 Epoch 99/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6478 - accuracy: 0.7287 - val_loss: 1.1616 - val_accuracy: 0.5539 Epoch 100/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.7266 - val_loss: 1.1475 - val_accuracy: 0.5245
HIST_TANDF = pd.DataFrame.from_dict(HISTORY_TANH.history)
y_pred = NN_MODEL_2.predict(x_test_s)
print('SCORE:',metrics.r2_score(y_test_c,y_pred))
print('\n\n',HIST_TANDF.sort_values('val_accuracy',ascending = False).head())
13/13 [==============================] - 0s 3ms/step
SCORE: -0.06490347271279912
loss accuracy val_loss val_accuracy
6 0.957873 0.594111 0.973629 0.615196
9 0.919990 0.617245 0.966579 0.615196
29 0.832676 0.647739 1.017645 0.610294
37 0.817218 0.635121 0.988155 0.610294
69 0.735600 0.702419 1.083075 0.605392
# ADDING MORE HIDDEN LAYERS TO THE DESIGN AND TEST AND TRAIN THE MODEL
NN_MODEL_3 = Sequential()
NN_MODEL_3.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='relu'))
NN_MODEL_3.add(tf.keras.layers.BatchNormalization())
NN_MODEL_3.add(Dense(128, kernel_initializer='normal', activation='relu'))
NN_MODEL_3.add(Dense(128, kernel_initializer='normal', activation='relu')) #ADDED HIDDEN LAYER
NN_MODEL_3.add(Dense(128, kernel_initializer='normal', activation='relu')) #ADDED HIDDEN LAYER
NN_MODEL_3.add(Dense(128, kernel_initializer='normal'))
NN_MODEL_3.add(LeakyReLU(alpha=0.1))
NN_MODEL_3.add(Dense(9, activation ="softmax"))
NN_MODEL_3.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY_ADDNL = NN_MODEL_3.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 2s 9ms/step - loss: 1.9506 - accuracy: 0.3891 - val_loss: 1.8068 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 1s 8ms/step - loss: 1.4253 - accuracy: 0.4869 - val_loss: 1.4698 - val_accuracy: 0.3775 Epoch 3/100 96/96 [==============================] - 1s 6ms/step - loss: 1.2206 - accuracy: 0.5457 - val_loss: 1.2842 - val_accuracy: 0.5564 Epoch 4/100 96/96 [==============================] - 1s 6ms/step - loss: 1.1419 - accuracy: 0.5521 - val_loss: 1.1501 - val_accuracy: 0.5784 Epoch 5/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0745 - accuracy: 0.5731 - val_loss: 1.0323 - val_accuracy: 0.5956 Epoch 6/100 96/96 [==============================] - 1s 5ms/step - loss: 1.0308 - accuracy: 0.5678 - val_loss: 0.9955 - val_accuracy: 0.5931 Epoch 7/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0161 - accuracy: 0.5815 - val_loss: 0.9745 - val_accuracy: 0.5980 Epoch 8/100 96/96 [==============================] - 1s 5ms/step - loss: 0.9975 - accuracy: 0.5752 - val_loss: 0.9733 - val_accuracy: 0.5931 Epoch 9/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9749 - accuracy: 0.6109 - val_loss: 0.9790 - val_accuracy: 0.6054 Epoch 10/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9527 - accuracy: 0.6183 - val_loss: 0.9789 - val_accuracy: 0.5956 Epoch 11/100 96/96 [==============================] - 1s 7ms/step - loss: 0.9370 - accuracy: 0.6067 - val_loss: 0.9846 - val_accuracy: 0.5858 Epoch 12/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9413 - accuracy: 0.6130 - val_loss: 0.9825 - val_accuracy: 0.5931 Epoch 13/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9254 - accuracy: 0.6372 - val_loss: 0.9776 - val_accuracy: 0.5907 Epoch 14/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9216 - accuracy: 0.6120 - val_loss: 1.0297 - val_accuracy: 0.5515 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9073 - accuracy: 0.6204 - val_loss: 1.0409 - val_accuracy: 0.5662 Epoch 16/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9126 - accuracy: 0.6320 - val_loss: 1.0109 - val_accuracy: 0.5956 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8979 - accuracy: 0.6341 - val_loss: 1.0095 - val_accuracy: 0.5907 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8894 - accuracy: 0.6330 - val_loss: 0.9749 - val_accuracy: 0.5882 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8733 - accuracy: 0.6404 - val_loss: 1.1000 - val_accuracy: 0.5392 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8701 - accuracy: 0.6498 - val_loss: 0.9964 - val_accuracy: 0.6127 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8570 - accuracy: 0.6614 - val_loss: 1.0166 - val_accuracy: 0.5784 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8393 - accuracy: 0.6635 - val_loss: 1.0621 - val_accuracy: 0.5907 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8310 - accuracy: 0.6509 - val_loss: 1.0336 - val_accuracy: 0.6103 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8122 - accuracy: 0.6667 - val_loss: 1.0496 - val_accuracy: 0.6152 Epoch 25/100 96/96 [==============================] - 1s 7ms/step - loss: 0.8138 - accuracy: 0.6698 - val_loss: 1.0315 - val_accuracy: 0.5980 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8091 - accuracy: 0.6772 - val_loss: 1.0747 - val_accuracy: 0.5882 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7752 - accuracy: 0.7014 - val_loss: 1.0641 - val_accuracy: 0.6054 Epoch 28/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7776 - accuracy: 0.6930 - val_loss: 1.0774 - val_accuracy: 0.5784 Epoch 29/100 96/96 [==============================] - 1s 5ms/step - loss: 0.7626 - accuracy: 0.6961 - val_loss: 1.0931 - val_accuracy: 0.5784 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7476 - accuracy: 0.6845 - val_loss: 1.1248 - val_accuracy: 0.5907 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7808 - accuracy: 0.6961 - val_loss: 1.0818 - val_accuracy: 0.5833 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7671 - accuracy: 0.6951 - val_loss: 1.2523 - val_accuracy: 0.5343 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7341 - accuracy: 0.6919 - val_loss: 1.1568 - val_accuracy: 0.5784 Epoch 34/100 96/96 [==============================] - 1s 7ms/step - loss: 0.7236 - accuracy: 0.7077 - val_loss: 1.1487 - val_accuracy: 0.5931 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7191 - accuracy: 0.7119 - val_loss: 1.1252 - val_accuracy: 0.6103 Epoch 36/100 96/96 [==============================] - 1s 7ms/step - loss: 0.7137 - accuracy: 0.7045 - val_loss: 1.2340 - val_accuracy: 0.5392 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7043 - accuracy: 0.7140 - val_loss: 1.2040 - val_accuracy: 0.5907 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7120 - accuracy: 0.7024 - val_loss: 1.1534 - val_accuracy: 0.6029 Epoch 39/100 96/96 [==============================] - 1s 7ms/step - loss: 0.6713 - accuracy: 0.7413 - val_loss: 1.1629 - val_accuracy: 0.6005 Epoch 40/100 96/96 [==============================] - 1s 7ms/step - loss: 0.6969 - accuracy: 0.7340 - val_loss: 1.1164 - val_accuracy: 0.6152 Epoch 41/100 96/96 [==============================] - 1s 7ms/step - loss: 0.6825 - accuracy: 0.7245 - val_loss: 1.1772 - val_accuracy: 0.5907 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6607 - accuracy: 0.7382 - val_loss: 1.2465 - val_accuracy: 0.6078 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6453 - accuracy: 0.7361 - val_loss: 1.2343 - val_accuracy: 0.5907 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6587 - accuracy: 0.7319 - val_loss: 1.2593 - val_accuracy: 0.5711 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6451 - accuracy: 0.7350 - val_loss: 1.3592 - val_accuracy: 0.5245 Epoch 46/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6465 - accuracy: 0.7266 - val_loss: 1.2462 - val_accuracy: 0.5637 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6231 - accuracy: 0.7361 - val_loss: 1.3085 - val_accuracy: 0.5270 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6155 - accuracy: 0.7571 - val_loss: 1.4453 - val_accuracy: 0.4853 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6429 - accuracy: 0.7476 - val_loss: 1.2925 - val_accuracy: 0.5196 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6150 - accuracy: 0.7508 - val_loss: 1.3124 - val_accuracy: 0.6078 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6083 - accuracy: 0.7592 - val_loss: 1.2717 - val_accuracy: 0.5833 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6186 - accuracy: 0.7497 - val_loss: 1.2466 - val_accuracy: 0.5956 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5953 - accuracy: 0.7697 - val_loss: 1.4691 - val_accuracy: 0.5196 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5843 - accuracy: 0.7603 - val_loss: 1.3142 - val_accuracy: 0.5368 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5635 - accuracy: 0.7760 - val_loss: 1.4566 - val_accuracy: 0.5343 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5833 - accuracy: 0.7750 - val_loss: 1.3878 - val_accuracy: 0.5539 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5999 - accuracy: 0.7560 - val_loss: 1.4036 - val_accuracy: 0.5809 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5813 - accuracy: 0.7823 - val_loss: 1.4238 - val_accuracy: 0.5294 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5595 - accuracy: 0.7760 - val_loss: 1.6133 - val_accuracy: 0.4755 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5539 - accuracy: 0.7802 - val_loss: 1.3709 - val_accuracy: 0.5711 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5270 - accuracy: 0.7907 - val_loss: 1.4212 - val_accuracy: 0.5270 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5491 - accuracy: 0.7581 - val_loss: 1.4568 - val_accuracy: 0.5343 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5425 - accuracy: 0.7697 - val_loss: 1.5169 - val_accuracy: 0.5490 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5494 - accuracy: 0.7886 - val_loss: 1.5427 - val_accuracy: 0.5858 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5190 - accuracy: 0.7918 - val_loss: 1.5154 - val_accuracy: 0.5441 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5444 - accuracy: 0.7729 - val_loss: 1.5148 - val_accuracy: 0.5172 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5137 - accuracy: 0.7813 - val_loss: 1.4747 - val_accuracy: 0.5956 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5160 - accuracy: 0.7960 - val_loss: 1.4526 - val_accuracy: 0.5539 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5305 - accuracy: 0.7781 - val_loss: 1.4872 - val_accuracy: 0.5564 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5097 - accuracy: 0.7992 - val_loss: 1.6220 - val_accuracy: 0.5809 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5026 - accuracy: 0.8044 - val_loss: 1.4958 - val_accuracy: 0.5662 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4929 - accuracy: 0.7971 - val_loss: 1.6512 - val_accuracy: 0.4853 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4871 - accuracy: 0.7950 - val_loss: 1.5895 - val_accuracy: 0.5539 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4709 - accuracy: 0.8044 - val_loss: 1.5852 - val_accuracy: 0.5931 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4599 - accuracy: 0.8170 - val_loss: 1.4847 - val_accuracy: 0.5564 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4505 - accuracy: 0.8170 - val_loss: 1.6270 - val_accuracy: 0.5907 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4575 - accuracy: 0.8212 - val_loss: 1.5620 - val_accuracy: 0.5539 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4732 - accuracy: 0.8107 - val_loss: 1.6171 - val_accuracy: 0.5760 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4621 - accuracy: 0.7918 - val_loss: 1.6559 - val_accuracy: 0.5637 Epoch 80/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4772 - accuracy: 0.8107 - val_loss: 1.4929 - val_accuracy: 0.5711 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4453 - accuracy: 0.8265 - val_loss: 1.5775 - val_accuracy: 0.5319 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4123 - accuracy: 0.8465 - val_loss: 1.6681 - val_accuracy: 0.5539 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4379 - accuracy: 0.8328 - val_loss: 1.6823 - val_accuracy: 0.5711 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4880 - accuracy: 0.7907 - val_loss: 1.7738 - val_accuracy: 0.5564 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4154 - accuracy: 0.8212 - val_loss: 1.7860 - val_accuracy: 0.4828 Epoch 86/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4161 - accuracy: 0.8391 - val_loss: 1.6395 - val_accuracy: 0.5368 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4502 - accuracy: 0.8244 - val_loss: 1.7174 - val_accuracy: 0.5711 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4699 - accuracy: 0.8044 - val_loss: 1.6753 - val_accuracy: 0.5098 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4258 - accuracy: 0.8423 - val_loss: 1.6757 - val_accuracy: 0.5417 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4351 - accuracy: 0.8202 - val_loss: 1.4857 - val_accuracy: 0.5711 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4167 - accuracy: 0.8307 - val_loss: 1.5802 - val_accuracy: 0.5613 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3982 - accuracy: 0.8360 - val_loss: 1.6150 - val_accuracy: 0.5613 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4336 - accuracy: 0.8233 - val_loss: 1.6231 - val_accuracy: 0.5882 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4124 - accuracy: 0.8307 - val_loss: 1.6378 - val_accuracy: 0.5858 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3889 - accuracy: 0.8433 - val_loss: 1.6919 - val_accuracy: 0.5613 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3802 - accuracy: 0.8507 - val_loss: 1.8055 - val_accuracy: 0.5613 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3831 - accuracy: 0.8381 - val_loss: 1.7349 - val_accuracy: 0.5490 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3908 - accuracy: 0.8339 - val_loss: 1.7967 - val_accuracy: 0.5196 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3981 - accuracy: 0.8644 - val_loss: 1.7281 - val_accuracy: 0.5637 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 0.3675 - accuracy: 0.8528 - val_loss: 1.8720 - val_accuracy: 0.5662
HIST_ADDNLDF = pd.DataFrame.from_dict(HISTORY_ADDNL.history)
y_pred = NN_MODEL_3.predict(x_test_s)
print('SCORE:',metrics.r2_score(y_test_c,y_pred))
print('\n\n',HIST_ADDNLDF.sort_values('val_accuracy',ascending = False).head())
13/13 [==============================] - 0s 7ms/step
SCORE: -0.0843767302916723
loss accuracy val_loss val_accuracy
39 0.696929 0.733964 1.116447 0.615196
23 0.812213 0.666667 1.049551 0.615196
19 0.870091 0.649842 0.996443 0.612745
22 0.830993 0.650894 1.033574 0.610294
34 0.719130 0.711882 1.125233 0.610294
# REMOVE HIDDEN LAYERS TO THE DESIGN AND TEST AND TRAIN THE MODEL
NN_MODEL_4 = Sequential()
NN_MODEL_4.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='relu'))
NN_MODEL_4.add(tf.keras.layers.BatchNormalization())
# NO HIDDEN LAYERS ADDED
NN_MODEL_4.add(Dense(9, activation ="softmax"))
NN_MODEL_4.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY_NONL = NN_MODEL_4.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 2s 8ms/step - loss: 1.8850 - accuracy: 0.4364 - val_loss: 1.6985 - val_accuracy: 0.5466 Epoch 2/100 96/96 [==============================] - 0s 5ms/step - loss: 1.3641 - accuracy: 0.5720 - val_loss: 1.3972 - val_accuracy: 0.5270 Epoch 3/100 96/96 [==============================] - 0s 5ms/step - loss: 1.1980 - accuracy: 0.5868 - val_loss: 1.2745 - val_accuracy: 0.5098 Epoch 4/100 96/96 [==============================] - 0s 4ms/step - loss: 1.0817 - accuracy: 0.5983 - val_loss: 1.1485 - val_accuracy: 0.5637 Epoch 5/100 96/96 [==============================] - 0s 4ms/step - loss: 0.9941 - accuracy: 0.6204 - val_loss: 1.1583 - val_accuracy: 0.5760 Epoch 6/100 96/96 [==============================] - 0s 5ms/step - loss: 0.9569 - accuracy: 0.6341 - val_loss: 1.1569 - val_accuracy: 0.5686 Epoch 7/100 96/96 [==============================] - 0s 5ms/step - loss: 0.9072 - accuracy: 0.6425 - val_loss: 1.1564 - val_accuracy: 0.5539 Epoch 8/100 96/96 [==============================] - 0s 5ms/step - loss: 0.8888 - accuracy: 0.6540 - val_loss: 1.1339 - val_accuracy: 0.5343 Epoch 9/100 96/96 [==============================] - 1s 5ms/step - loss: 0.8684 - accuracy: 0.6625 - val_loss: 1.2089 - val_accuracy: 0.5270 Epoch 10/100 96/96 [==============================] - 0s 5ms/step - loss: 0.8304 - accuracy: 0.6688 - val_loss: 1.1097 - val_accuracy: 0.5858 Epoch 11/100 96/96 [==============================] - 0s 5ms/step - loss: 0.8212 - accuracy: 0.6698 - val_loss: 1.0914 - val_accuracy: 0.5613 Epoch 12/100 96/96 [==============================] - 0s 5ms/step - loss: 0.8088 - accuracy: 0.6667 - val_loss: 1.1361 - val_accuracy: 0.5662 Epoch 13/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7821 - accuracy: 0.6919 - val_loss: 1.1697 - val_accuracy: 0.5392 Epoch 14/100 96/96 [==============================] - 0s 5ms/step - loss: 0.8086 - accuracy: 0.6593 - val_loss: 1.1317 - val_accuracy: 0.5221 Epoch 15/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7791 - accuracy: 0.6730 - val_loss: 1.1818 - val_accuracy: 0.5392 Epoch 16/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7901 - accuracy: 0.6667 - val_loss: 1.1105 - val_accuracy: 0.5539 Epoch 17/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7647 - accuracy: 0.6761 - val_loss: 1.1029 - val_accuracy: 0.5490 Epoch 18/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7458 - accuracy: 0.6866 - val_loss: 1.0862 - val_accuracy: 0.5784 Epoch 19/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7312 - accuracy: 0.7035 - val_loss: 1.1658 - val_accuracy: 0.5196 Epoch 20/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7220 - accuracy: 0.6982 - val_loss: 1.1949 - val_accuracy: 0.5074 Epoch 21/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6957 - accuracy: 0.7192 - val_loss: 1.0961 - val_accuracy: 0.5490 Epoch 22/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7007 - accuracy: 0.7203 - val_loss: 1.1188 - val_accuracy: 0.5637 Epoch 23/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7242 - accuracy: 0.6835 - val_loss: 1.1703 - val_accuracy: 0.5172 Epoch 24/100 96/96 [==============================] - 0s 5ms/step - loss: 0.7097 - accuracy: 0.7119 - val_loss: 1.1111 - val_accuracy: 0.5735 Epoch 25/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.7234 - val_loss: 1.1420 - val_accuracy: 0.5466 Epoch 26/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6877 - accuracy: 0.7087 - val_loss: 1.1721 - val_accuracy: 0.5196 Epoch 27/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6695 - accuracy: 0.7308 - val_loss: 1.2121 - val_accuracy: 0.5172 Epoch 28/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6853 - accuracy: 0.7150 - val_loss: 1.2148 - val_accuracy: 0.5098 Epoch 29/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6609 - accuracy: 0.7319 - val_loss: 1.1380 - val_accuracy: 0.5735 Epoch 30/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6307 - accuracy: 0.7487 - val_loss: 1.2033 - val_accuracy: 0.5441 Epoch 31/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6571 - accuracy: 0.7319 - val_loss: 1.1828 - val_accuracy: 0.5809 Epoch 32/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6887 - accuracy: 0.7077 - val_loss: 1.1965 - val_accuracy: 0.5319 Epoch 33/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6679 - accuracy: 0.7161 - val_loss: 1.1538 - val_accuracy: 0.5564 Epoch 34/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6339 - accuracy: 0.7403 - val_loss: 1.1960 - val_accuracy: 0.5588 Epoch 35/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6251 - accuracy: 0.7434 - val_loss: 1.2156 - val_accuracy: 0.5588 Epoch 36/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6463 - accuracy: 0.7413 - val_loss: 1.2456 - val_accuracy: 0.4975 Epoch 37/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6621 - accuracy: 0.7298 - val_loss: 1.2080 - val_accuracy: 0.5490 Epoch 38/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6461 - accuracy: 0.7192 - val_loss: 1.1608 - val_accuracy: 0.5392 Epoch 39/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6517 - accuracy: 0.7266 - val_loss: 1.2534 - val_accuracy: 0.5196 Epoch 40/100 96/96 [==============================] - 0s 5ms/step - loss: 0.6508 - accuracy: 0.7403 - val_loss: 1.1586 - val_accuracy: 0.5392 Epoch 41/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5989 - accuracy: 0.7571 - val_loss: 1.1512 - val_accuracy: 0.5564 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5880 - accuracy: 0.7655 - val_loss: 1.2703 - val_accuracy: 0.5098 Epoch 43/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6137 - accuracy: 0.7403 - val_loss: 1.2016 - val_accuracy: 0.5490 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5823 - accuracy: 0.7634 - val_loss: 1.2008 - val_accuracy: 0.5294 Epoch 45/100 96/96 [==============================] - 1s 5ms/step - loss: 0.6234 - accuracy: 0.7497 - val_loss: 1.1895 - val_accuracy: 0.5417 Epoch 46/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5811 - accuracy: 0.7729 - val_loss: 1.2145 - val_accuracy: 0.5539 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5756 - accuracy: 0.7792 - val_loss: 1.2386 - val_accuracy: 0.5319 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6018 - accuracy: 0.7424 - val_loss: 1.2214 - val_accuracy: 0.5417 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5899 - accuracy: 0.7560 - val_loss: 1.2227 - val_accuracy: 0.5466 Epoch 50/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5828 - accuracy: 0.7645 - val_loss: 1.1861 - val_accuracy: 0.5613 Epoch 51/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5838 - accuracy: 0.7729 - val_loss: 1.2065 - val_accuracy: 0.5172 Epoch 52/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5828 - accuracy: 0.7560 - val_loss: 1.2968 - val_accuracy: 0.5270 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5623 - accuracy: 0.7823 - val_loss: 1.2344 - val_accuracy: 0.5466 Epoch 54/100 96/96 [==============================] - 1s 8ms/step - loss: 0.5473 - accuracy: 0.7687 - val_loss: 1.2443 - val_accuracy: 0.5074 Epoch 55/100 96/96 [==============================] - 0s 4ms/step - loss: 0.5860 - accuracy: 0.7687 - val_loss: 1.2151 - val_accuracy: 0.5564 Epoch 56/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5472 - accuracy: 0.7781 - val_loss: 1.2366 - val_accuracy: 0.5564 Epoch 57/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5790 - accuracy: 0.7518 - val_loss: 1.3396 - val_accuracy: 0.5221 Epoch 58/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5384 - accuracy: 0.7855 - val_loss: 1.2871 - val_accuracy: 0.5098 Epoch 59/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5707 - accuracy: 0.7750 - val_loss: 1.1857 - val_accuracy: 0.5172 Epoch 60/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5581 - accuracy: 0.7697 - val_loss: 1.2393 - val_accuracy: 0.5392 Epoch 61/100 96/96 [==============================] - 0s 4ms/step - loss: 0.5577 - accuracy: 0.7739 - val_loss: 1.2375 - val_accuracy: 0.5319 Epoch 62/100 96/96 [==============================] - 0s 4ms/step - loss: 0.5198 - accuracy: 0.7886 - val_loss: 1.2488 - val_accuracy: 0.5270 Epoch 63/100 96/96 [==============================] - 1s 7ms/step - loss: 0.5455 - accuracy: 0.7876 - val_loss: 1.2388 - val_accuracy: 0.5392 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5425 - accuracy: 0.7897 - val_loss: 1.2765 - val_accuracy: 0.5025 Epoch 65/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5157 - accuracy: 0.7971 - val_loss: 1.2617 - val_accuracy: 0.5172 Epoch 66/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5516 - accuracy: 0.7781 - val_loss: 1.2026 - val_accuracy: 0.5711 Epoch 67/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5411 - accuracy: 0.7865 - val_loss: 1.2355 - val_accuracy: 0.5417 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5215 - accuracy: 0.7918 - val_loss: 1.2139 - val_accuracy: 0.5539 Epoch 69/100 96/96 [==============================] - 0s 4ms/step - loss: 0.5467 - accuracy: 0.7992 - val_loss: 1.2816 - val_accuracy: 0.5123 Epoch 70/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5221 - accuracy: 0.7886 - val_loss: 1.2892 - val_accuracy: 0.5196 Epoch 71/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5151 - accuracy: 0.8034 - val_loss: 1.3709 - val_accuracy: 0.5368 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5149 - accuracy: 0.8013 - val_loss: 1.3230 - val_accuracy: 0.5711 Epoch 73/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5022 - accuracy: 0.8034 - val_loss: 1.2657 - val_accuracy: 0.5392 Epoch 74/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4995 - accuracy: 0.7918 - val_loss: 1.2942 - val_accuracy: 0.5809 Epoch 75/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4952 - accuracy: 0.8023 - val_loss: 1.2749 - val_accuracy: 0.5564 Epoch 76/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4705 - accuracy: 0.8191 - val_loss: 1.3049 - val_accuracy: 0.5417 Epoch 77/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4827 - accuracy: 0.8023 - val_loss: 1.3438 - val_accuracy: 0.5172 Epoch 78/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4753 - accuracy: 0.8212 - val_loss: 1.3328 - val_accuracy: 0.5466 Epoch 79/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4945 - accuracy: 0.8065 - val_loss: 1.2015 - val_accuracy: 0.5711 Epoch 80/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4836 - accuracy: 0.7971 - val_loss: 1.3316 - val_accuracy: 0.5123 Epoch 81/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4850 - accuracy: 0.8086 - val_loss: 1.2334 - val_accuracy: 0.5417 Epoch 82/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5041 - accuracy: 0.8097 - val_loss: 1.2793 - val_accuracy: 0.5368 Epoch 83/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4989 - accuracy: 0.8149 - val_loss: 1.2289 - val_accuracy: 0.5735 Epoch 84/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5092 - accuracy: 0.7907 - val_loss: 1.2245 - val_accuracy: 0.5564 Epoch 85/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4898 - accuracy: 0.8097 - val_loss: 1.2621 - val_accuracy: 0.5147 Epoch 86/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4755 - accuracy: 0.8202 - val_loss: 1.3649 - val_accuracy: 0.5392 Epoch 87/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5078 - accuracy: 0.7907 - val_loss: 1.2872 - val_accuracy: 0.5392 Epoch 88/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4906 - accuracy: 0.8023 - val_loss: 1.2942 - val_accuracy: 0.5392 Epoch 89/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4565 - accuracy: 0.8202 - val_loss: 1.2631 - val_accuracy: 0.5441 Epoch 90/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4749 - accuracy: 0.8034 - val_loss: 1.3268 - val_accuracy: 0.5270 Epoch 91/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4466 - accuracy: 0.8254 - val_loss: 1.2010 - val_accuracy: 0.5613 Epoch 92/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4800 - accuracy: 0.8023 - val_loss: 1.2944 - val_accuracy: 0.5490 Epoch 93/100 96/96 [==============================] - 0s 5ms/step - loss: 0.5169 - accuracy: 0.8023 - val_loss: 1.2163 - val_accuracy: 0.5686 Epoch 94/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4882 - accuracy: 0.8002 - val_loss: 1.2334 - val_accuracy: 0.5441 Epoch 95/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4668 - accuracy: 0.8233 - val_loss: 1.3008 - val_accuracy: 0.5270 Epoch 96/100 96/96 [==============================] - 1s 5ms/step - loss: 0.4433 - accuracy: 0.8339 - val_loss: 1.3123 - val_accuracy: 0.5294 Epoch 97/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4823 - accuracy: 0.8065 - val_loss: 1.2696 - val_accuracy: 0.5564 Epoch 98/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4581 - accuracy: 0.8233 - val_loss: 1.3092 - val_accuracy: 0.5466 Epoch 99/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4507 - accuracy: 0.8297 - val_loss: 1.3362 - val_accuracy: 0.5490 Epoch 100/100 96/96 [==============================] - 0s 5ms/step - loss: 0.4784 - accuracy: 0.8086 - val_loss: 1.4951 - val_accuracy: 0.5000
HIST_NONLDF = pd.DataFrame.from_dict(HISTORY_NONL.history)
y_pred = NN_MODEL_4.predict(x_test_s)
print('SCORE:',metrics.r2_score(y_test_c,y_pred))
print('\n\n',HIST_NONLDF.sort_values('val_accuracy',ascending = False).head())
13/13 [==============================] - 0s 6ms/step
SCORE: -0.17272356089962346
loss accuracy val_loss val_accuracy
9 0.830406 0.668770 1.109689 0.585784
73 0.499462 0.791798 1.294200 0.580882
30 0.657135 0.731861 1.182817 0.580882
17 0.745816 0.686646 1.086183 0.578431
4 0.994086 0.620400 1.158261 0.575980
* BY ADDING ADDITIONAL LAYERS TO THE MODEL, ACCURACY FOR BOTH TRAINING AND TESTING DATA IS GOOD.
* ALSO THE LOSS ON TRAINING AND TESTING DATA REDUCED BY ADDING ADDITIONAL LAYERS.
* BY ADDING MORE LAYERS WE CAN EXPECT ACCURACY CAN BE INCREASED AND LOSS CAN BE REDUCED.
# ADDING MORE HIDDEN LAYERS TO THE DESIGN AND TEST AND TRAIN THE MODEL
NN_MODEL_5 = Sequential()
NN_MODEL_5.add(Dense(256, kernel_initializer='normal',input_dim = x_train_s.shape[1], activation='relu'))
NN_MODEL_5.add(tf.keras.layers.BatchNormalization())
NN_MODEL_5.add(Dense(128, kernel_initializer='normal', activation='relu'))
NN_MODEL_5.add(Dense(128, kernel_initializer='normal', activation='relu')) #ADDED HIDDEN LAYER
NN_MODEL_5.add(Dense(128, kernel_initializer='normal', activation='relu')) #ADDED HIDDEN LAYER
NN_MODEL_5.add(Dense(128, kernel_initializer='normal')) #ADDED HIDDEN LAYER
NN_MODEL_5.add(LeakyReLU(alpha=0.1))
NN_MODEL_5.add(Dense(128, kernel_initializer='normal')) #ADDED HIDDEN LAYER
NN_MODEL_5.add(LeakyReLU(alpha=0.1))
NN_MODEL_5.add(Dense(128, kernel_initializer='normal'))
NN_MODEL_5.add(LeakyReLU(alpha=0.1))
NN_MODEL_5.add(Dense(9, activation ="softmax"))
NN_MODEL_5.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY_ADDNL = NN_MODEL_5.fit(x_train_s, y_train_c, validation_data=(x_test_s,y_test_c),epochs=100, batch_size=10)
Epoch 1/100 96/96 [==============================] - 5s 27ms/step - loss: 2.0138 - accuracy: 0.4006 - val_loss: 1.8267 - val_accuracy: 0.3652 Epoch 2/100 96/96 [==============================] - 2s 17ms/step - loss: 1.6859 - accuracy: 0.4090 - val_loss: 1.5406 - val_accuracy: 0.3652 Epoch 3/100 96/96 [==============================] - 1s 12ms/step - loss: 1.4602 - accuracy: 0.4059 - val_loss: 1.3570 - val_accuracy: 0.3652 Epoch 4/100 96/96 [==============================] - 1s 13ms/step - loss: 1.3393 - accuracy: 0.4069 - val_loss: 1.2675 - val_accuracy: 0.3652 Epoch 5/100 96/96 [==============================] - 1s 13ms/step - loss: 1.2902 - accuracy: 0.4164 - val_loss: 1.2227 - val_accuracy: 0.4975 Epoch 6/100 96/96 [==============================] - 1s 11ms/step - loss: 1.2636 - accuracy: 0.4501 - val_loss: 1.1996 - val_accuracy: 0.5564 Epoch 7/100 96/96 [==============================] - 1s 11ms/step - loss: 1.2454 - accuracy: 0.4606 - val_loss: 1.1977 - val_accuracy: 0.3652 Epoch 8/100 96/96 [==============================] - 1s 11ms/step - loss: 1.2320 - accuracy: 0.4774 - val_loss: 1.1592 - val_accuracy: 0.5417 Epoch 9/100 96/96 [==============================] - 1s 11ms/step - loss: 1.2064 - accuracy: 0.5152 - val_loss: 1.1330 - val_accuracy: 0.5711 Epoch 10/100 96/96 [==============================] - 1s 14ms/step - loss: 1.1615 - accuracy: 0.5426 - val_loss: 1.0930 - val_accuracy: 0.5711 Epoch 11/100 96/96 [==============================] - 1s 14ms/step - loss: 1.1094 - accuracy: 0.5605 - val_loss: 1.0485 - val_accuracy: 0.5686 Epoch 12/100 96/96 [==============================] - 2s 16ms/step - loss: 1.0733 - accuracy: 0.5689 - val_loss: 1.0019 - val_accuracy: 0.5760 Epoch 13/100 96/96 [==============================] - 1s 14ms/step - loss: 1.0604 - accuracy: 0.5573 - val_loss: 1.0165 - val_accuracy: 0.5931 Epoch 14/100 96/96 [==============================] - 1s 10ms/step - loss: 1.0298 - accuracy: 0.5626 - val_loss: 1.0021 - val_accuracy: 0.5833 Epoch 15/100 96/96 [==============================] - 1s 6ms/step - loss: 1.0106 - accuracy: 0.5889 - val_loss: 1.0382 - val_accuracy: 0.5686 Epoch 16/100 96/96 [==============================] - 1s 7ms/step - loss: 1.0220 - accuracy: 0.5846 - val_loss: 0.9985 - val_accuracy: 0.5956 Epoch 17/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9910 - accuracy: 0.5931 - val_loss: 1.0214 - val_accuracy: 0.5711 Epoch 18/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9837 - accuracy: 0.6120 - val_loss: 1.0241 - val_accuracy: 0.5907 Epoch 19/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9817 - accuracy: 0.6025 - val_loss: 1.0051 - val_accuracy: 0.5980 Epoch 20/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9712 - accuracy: 0.6067 - val_loss: 1.0163 - val_accuracy: 0.5931 Epoch 21/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9579 - accuracy: 0.6141 - val_loss: 1.0752 - val_accuracy: 0.5613 Epoch 22/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9376 - accuracy: 0.6299 - val_loss: 1.0243 - val_accuracy: 0.5882 Epoch 23/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9267 - accuracy: 0.6351 - val_loss: 1.0394 - val_accuracy: 0.5686 Epoch 24/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9241 - accuracy: 0.6288 - val_loss: 1.0222 - val_accuracy: 0.6127 Epoch 25/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9167 - accuracy: 0.6383 - val_loss: 1.0238 - val_accuracy: 0.5858 Epoch 26/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8951 - accuracy: 0.6498 - val_loss: 1.0238 - val_accuracy: 0.6054 Epoch 27/100 96/96 [==============================] - 1s 6ms/step - loss: 0.9001 - accuracy: 0.6362 - val_loss: 1.0875 - val_accuracy: 0.5490 Epoch 28/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8721 - accuracy: 0.6509 - val_loss: 1.0450 - val_accuracy: 0.5907 Epoch 29/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8864 - accuracy: 0.6540 - val_loss: 1.0600 - val_accuracy: 0.5907 Epoch 30/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8831 - accuracy: 0.6540 - val_loss: 1.0503 - val_accuracy: 0.5956 Epoch 31/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8643 - accuracy: 0.6761 - val_loss: 1.0867 - val_accuracy: 0.5735 Epoch 32/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8532 - accuracy: 0.6740 - val_loss: 1.1215 - val_accuracy: 0.5564 Epoch 33/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8816 - accuracy: 0.6635 - val_loss: 1.0111 - val_accuracy: 0.6250 Epoch 34/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8563 - accuracy: 0.6604 - val_loss: 1.0491 - val_accuracy: 0.5711 Epoch 35/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8580 - accuracy: 0.6667 - val_loss: 1.0731 - val_accuracy: 0.5637 Epoch 36/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8028 - accuracy: 0.6919 - val_loss: 1.0842 - val_accuracy: 0.5907 Epoch 37/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8484 - accuracy: 0.6688 - val_loss: 1.1592 - val_accuracy: 0.5490 Epoch 38/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8240 - accuracy: 0.6803 - val_loss: 1.0734 - val_accuracy: 0.5858 Epoch 39/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8210 - accuracy: 0.6835 - val_loss: 1.0798 - val_accuracy: 0.5564 Epoch 40/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8116 - accuracy: 0.6856 - val_loss: 1.0701 - val_accuracy: 0.5931 Epoch 41/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8108 - accuracy: 0.6793 - val_loss: 1.0744 - val_accuracy: 0.5809 Epoch 42/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8075 - accuracy: 0.6866 - val_loss: 1.0975 - val_accuracy: 0.5882 Epoch 43/100 96/96 [==============================] - 1s 6ms/step - loss: 0.8014 - accuracy: 0.6951 - val_loss: 1.1460 - val_accuracy: 0.5588 Epoch 44/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7968 - accuracy: 0.6909 - val_loss: 1.1019 - val_accuracy: 0.5882 Epoch 45/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7663 - accuracy: 0.7014 - val_loss: 1.1485 - val_accuracy: 0.5711 Epoch 46/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7865 - accuracy: 0.6940 - val_loss: 1.1658 - val_accuracy: 0.6078 Epoch 47/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7719 - accuracy: 0.7024 - val_loss: 1.2239 - val_accuracy: 0.5515 Epoch 48/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7600 - accuracy: 0.7003 - val_loss: 1.2257 - val_accuracy: 0.5172 Epoch 49/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7491 - accuracy: 0.7119 - val_loss: 1.1808 - val_accuracy: 0.5956 Epoch 50/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7466 - accuracy: 0.7077 - val_loss: 1.1586 - val_accuracy: 0.5588 Epoch 51/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7294 - accuracy: 0.7087 - val_loss: 1.1681 - val_accuracy: 0.5613 Epoch 52/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7059 - accuracy: 0.7350 - val_loss: 1.1955 - val_accuracy: 0.6005 Epoch 53/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7204 - accuracy: 0.7150 - val_loss: 1.2766 - val_accuracy: 0.5490 Epoch 54/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7158 - accuracy: 0.7119 - val_loss: 1.2104 - val_accuracy: 0.5735 Epoch 55/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7270 - accuracy: 0.7077 - val_loss: 1.1713 - val_accuracy: 0.6127 Epoch 56/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7316 - accuracy: 0.7087 - val_loss: 1.1455 - val_accuracy: 0.5858 Epoch 57/100 96/96 [==============================] - 1s 6ms/step - loss: 0.7016 - accuracy: 0.7308 - val_loss: 1.1982 - val_accuracy: 0.5882 Epoch 58/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6924 - accuracy: 0.7340 - val_loss: 1.1837 - val_accuracy: 0.6029 Epoch 59/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6952 - accuracy: 0.7192 - val_loss: 1.1684 - val_accuracy: 0.5956 Epoch 60/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6756 - accuracy: 0.7424 - val_loss: 1.3547 - val_accuracy: 0.5490 Epoch 61/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6873 - accuracy: 0.7203 - val_loss: 1.2531 - val_accuracy: 0.5735 Epoch 62/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6882 - accuracy: 0.7098 - val_loss: 1.2579 - val_accuracy: 0.5907 Epoch 63/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6686 - accuracy: 0.7466 - val_loss: 1.2729 - val_accuracy: 0.5735 Epoch 64/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6527 - accuracy: 0.7424 - val_loss: 1.3423 - val_accuracy: 0.5833 Epoch 65/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6517 - accuracy: 0.7287 - val_loss: 1.3084 - val_accuracy: 0.5686 Epoch 66/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6498 - accuracy: 0.7445 - val_loss: 1.2639 - val_accuracy: 0.5735 Epoch 67/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6660 - accuracy: 0.7245 - val_loss: 1.2884 - val_accuracy: 0.5711 Epoch 68/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6760 - accuracy: 0.7287 - val_loss: 1.3572 - val_accuracy: 0.5221 Epoch 69/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6312 - accuracy: 0.7539 - val_loss: 1.2945 - val_accuracy: 0.5613 Epoch 70/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6220 - accuracy: 0.7539 - val_loss: 1.2689 - val_accuracy: 0.6029 Epoch 71/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6467 - accuracy: 0.7560 - val_loss: 1.2791 - val_accuracy: 0.5417 Epoch 72/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6204 - accuracy: 0.7581 - val_loss: 1.3291 - val_accuracy: 0.5515 Epoch 73/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6033 - accuracy: 0.7550 - val_loss: 1.4039 - val_accuracy: 0.5760 Epoch 74/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6164 - accuracy: 0.7603 - val_loss: 1.3561 - val_accuracy: 0.5490 Epoch 75/100 96/96 [==============================] - 1s 6ms/step - loss: 0.6212 - accuracy: 0.7403 - val_loss: 1.3342 - val_accuracy: 0.5735 Epoch 76/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5992 - accuracy: 0.7613 - val_loss: 1.3079 - val_accuracy: 0.5882 Epoch 77/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5990 - accuracy: 0.7581 - val_loss: 1.3568 - val_accuracy: 0.5784 Epoch 78/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5926 - accuracy: 0.7645 - val_loss: 1.3136 - val_accuracy: 0.6127 Epoch 79/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5788 - accuracy: 0.7697 - val_loss: 1.5968 - val_accuracy: 0.4755 Epoch 80/100 96/96 [==============================] - 1s 7ms/step - loss: 0.5608 - accuracy: 0.7844 - val_loss: 1.6064 - val_accuracy: 0.5245 Epoch 81/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5546 - accuracy: 0.7781 - val_loss: 1.4558 - val_accuracy: 0.6078 Epoch 82/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5577 - accuracy: 0.7823 - val_loss: 1.5292 - val_accuracy: 0.5858 Epoch 83/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5507 - accuracy: 0.7897 - val_loss: 1.4285 - val_accuracy: 0.5490 Epoch 84/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5079 - accuracy: 0.7992 - val_loss: 1.5192 - val_accuracy: 0.5490 Epoch 85/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5320 - accuracy: 0.7813 - val_loss: 1.4987 - val_accuracy: 0.5833 Epoch 86/100 96/96 [==============================] - 1s 5ms/step - loss: 0.5933 - accuracy: 0.7802 - val_loss: 1.5104 - val_accuracy: 0.5539 Epoch 87/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5536 - accuracy: 0.7834 - val_loss: 1.5356 - val_accuracy: 0.5686 Epoch 88/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5164 - accuracy: 0.7950 - val_loss: 1.5074 - val_accuracy: 0.5417 Epoch 89/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5235 - accuracy: 0.8002 - val_loss: 1.5468 - val_accuracy: 0.5539 Epoch 90/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4965 - accuracy: 0.8086 - val_loss: 1.6193 - val_accuracy: 0.5735 Epoch 91/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5097 - accuracy: 0.7992 - val_loss: 1.5611 - val_accuracy: 0.5809 Epoch 92/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5616 - accuracy: 0.7813 - val_loss: 1.4710 - val_accuracy: 0.5784 Epoch 93/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5084 - accuracy: 0.8086 - val_loss: 1.5231 - val_accuracy: 0.5588 Epoch 94/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5111 - accuracy: 0.8044 - val_loss: 1.4473 - val_accuracy: 0.5907 Epoch 95/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5087 - accuracy: 0.7928 - val_loss: 1.5449 - val_accuracy: 0.5711 Epoch 96/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4620 - accuracy: 0.8181 - val_loss: 1.7075 - val_accuracy: 0.5368 Epoch 97/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4992 - accuracy: 0.8065 - val_loss: 1.7163 - val_accuracy: 0.5245 Epoch 98/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4931 - accuracy: 0.8044 - val_loss: 1.6408 - val_accuracy: 0.5417 Epoch 99/100 96/96 [==============================] - 1s 6ms/step - loss: 0.5003 - accuracy: 0.8034 - val_loss: 1.5093 - val_accuracy: 0.5711 Epoch 100/100 96/96 [==============================] - 1s 6ms/step - loss: 0.4519 - accuracy: 0.8318 - val_loss: 1.6125 - val_accuracy: 0.5858
HIST_ADDNLDF = pd.DataFrame.from_dict(HISTORY_ADDNL.history)
y_pred = NN_MODEL_5.predict(x_test_s)
print('SCORE:',metrics.r2_score(y_test_c,y_pred))
print('\n\n',HIST_ADDNLDF.sort_values('val_accuracy',ascending = False).head())
13/13 [==============================] - 0s 3ms/step
SCORE: -0.042240137240989434
loss accuracy val_loss val_accuracy
32 0.881614 0.663512 1.011114 0.625000
54 0.727024 0.707676 1.171290 0.612745
77 0.592574 0.764458 1.313602 0.612745
23 0.924132 0.628812 1.022164 0.612745
45 0.786466 0.694006 1.165810 0.607843
* BY INCREASING THE EPOCHS, WE DONT SEE ANY ACTUAL INCREASE IN THE TRAINING OR TEST ACCURACY.
* BY INCREASING THE HIDDEN LAYERS, MODEL IS BECOMING MORE STABLE.
* AT TRAINING DATA ACCURACY OF 76.4% WE CAN SEE TEST DATA ACCURACY IS 61.27%
* BY ADDING MORE AND MORE LAYERS, WE CAN STILL INCREASE THE MODEL PERFORMANCE BUT MAY BECOME OVERFIT.
* WE WILL CONSIDER NN_MODEL5 AS THE UPDATED AND FINAL MODEL.
TRAINING_LOSS = HIST_ADDNLDF['loss']
VALIDATION_LOSS = HIST_ADDNLDF['val_loss']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_LOSS, 'r', label='TRAINING_LOSS')
plt.plot(EPOCHS, VALIDATION_LOSS, 'b', label='VALIDATION_LOSS')
plt.title('TRAINING AND VALIDATION LOSS COMPARISON PLOT')
plt.xlabel('EPOCHS')
plt.ylabel('LOSS')
plt.legend()
plt.show()
TRAINING_ACC = HIST_ADDNLDF['accuracy']
VALIDATION_ACC = HIST_ADDNLDF['val_accuracy']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_ACC, 'r', label='TRAINING_ACCURACY')
plt.plot(EPOCHS, VALIDATION_ACC, 'b', label='VALIDATION_ACCURACY')
plt.title('TRAINING AND VALIDATION ACCURACY COMPARISON PLOT')
plt.xlabel('EPOCHS')
plt.ylabel('ACCURACY')
plt.legend()
plt.show()
* INSIGHTS:
* INCREASING THE NUMBER OF EPOCHS DID NOT IMPROVED THE PERFORMANCE.
* VARIOUS OPTIMIZERS AND LEARNING RATES NEITHER IMPROVED THE PERFORMANCE.
* SIGMOID NAD TANH ACTIVATION FUNCTIONS IMPROVED THE PERFORMANCE SLIGHTLY
* AS THE HIDDEN LAYERS ARE INCREASED, WE CAN SEE THAT MODEL IS BECOMING STABLE.
* TRAINING DATA GRAPH IS SHOWN BECOMING MORE AND MORE CLEAR CURVE.
* LOSS IN TRAINING DATA IS DECREASED COMPARED TO OLD MODEL AND TRAINIG DATA ACCURACY IS SLIGHTLY INCREASED.
* WE CAN ADD MORE LAYERS TO THE DESIGN AND INCREASE THE MODEL PERFORMANCE.
import h5py
SVHN_DATA = h5py.File('Autonomous_Vehicles_SVHN_single_grey1.h5','r')
print('ALL THE KEYS PRESENT IN THE LOADED H5 FILE: \n',SVHN_DATA.keys())
ALL THE KEYS PRESENT IN THE LOADED H5 FILE: <KeysViewHDF5 ['X_test', 'X_train', 'X_val', 'y_test', 'y_train', 'y_val']>
X_TRAIN = SVHN_DATA['X_train'][:]
Y_TRAIN = SVHN_DATA['y_train'][:]
X_VAL = SVHN_DATA['X_val'][:]
Y_VAL = SVHN_DATA['y_val'][:]
X_TEST = SVHN_DATA['X_test'][:]
Y_TEST = SVHN_DATA['y_test'][:]
X_TRAIN[:5]
array([[[ 33.0704, 30.2601, 26.852 , ..., 71.4471, 58.2204,
42.9939],
[ 25.2283, 25.5533, 29.9765, ..., 113.0209, 103.3639,
84.2949],
[ 26.2775, 22.6137, 40.4763, ..., 113.3028, 121.775 ,
115.4228],
...,
[ 28.5502, 36.212 , 45.0801, ..., 24.1359, 25.0927,
26.0603],
[ 38.4352, 26.4733, 23.2717, ..., 28.1094, 29.4683,
30.0661],
[ 50.2984, 26.0773, 24.0389, ..., 49.6682, 50.853 ,
53.0377]],
[[ 86.9591, 87.0685, 88.3735, ..., 91.8014, 89.7477,
92.5302],
[ 86.688 , 86.9114, 87.4337, ..., 90.7306, 87.204 ,
88.5629],
[ 85.9654, 85.8145, 85.9239, ..., 63.8626, 59.8199,
54.8805],
...,
[ 90.2236, 91.0448, 93.4637, ..., 55.3535, 48.5822,
44.0557],
[ 90.6427, 90.4039, 90.937 , ..., 78.2696, 77.4977,
74.27 ],
[ 88.0236, 88.1977, 86.6709, ..., 75.2206, 76.6396,
79.2865]],
[[123.125 , 125.8581, 122.0757, ..., 123.5747, 124.1186,
123.3144],
[121.1683, 124.1294, 117.4613, ..., 115.6078, 119.5751,
122.8306],
[124.6132, 121.1019, 109.6623, ..., 111.1783, 119.7923,
124.7595],
...,
[135.1391, 127.3679, 117.754 , ..., 95.0919, 105.5917,
114.9283],
[134.8402, 131.9545, 124.0415, ..., 93.864 , 105.3036,
115.1132],
[134.8402, 132.0685, 128.34 , ..., 93.9349, 104.7875,
113.8252]],
[[147.6196, 139.6204, 142.6201, ..., 151.1631, 151.8641,
148.8644],
[146.6197, 137.6206, 140.9084, ..., 151.1631, 149.8643,
150.8642],
[145.6198, 136.9088, 139.9085, ..., 151.8641, 150.8642,
151.8641],
...,
[106.1785, 99.2393, 101.5981, ..., 157.9021, 156.8914,
156.8914],
[111.879 , 112.238 , 113.0099, ..., 157.1302, 156.8914,
157.1795],
[122.8779, 127.2365, 128.2364, ..., 157.1194, 157.1795,
157.1795]],
[[153.989 , 155.1783, 157.4276, ..., 102.065 , 117.9171,
129.0578],
[160.1024, 159.694 , 157.6556, ..., 98.7664, 114.2056,
122.4714],
[161.6292, 160.3349, 154.9979, ..., 96.2828, 108.4943,
115.7602],
...,
[169.6993, 165.4978, 154.4603, ..., 54.1544, 58.2033,
65.3767],
[172.699 , 168.4975, 158.047 , ..., 46.867 , 51.6169,
61.1491],
[176.6986, 172.4971, 160.6877, ..., 45.693 , 48.6172,
58.1494]]], dtype=float32)
print('SHAPE OF X_TRAIN: ',X_TRAIN.shape)
print('\nSHAPE OF X_TEST: ',X_TEST.shape)
print('\nSHAPE OF X_VAL: ',X_VAL.shape)
print('\nSHAPE OF Y_TRAIN: ',Y_TRAIN.shape)
print('\nSHAPE OF Y_TEST: ',Y_TEST.shape)
print('\nSHAPE OF Y_VAL: ',Y_VAL.shape)
SHAPE OF X_TRAIN: (42000, 32, 32) SHAPE OF X_TEST: (18000, 32, 32) SHAPE OF X_VAL: (60000, 32, 32) SHAPE OF Y_TRAIN: (42000,) SHAPE OF Y_TEST: (18000,) SHAPE OF Y_VAL: (60000,)
* X_TRAIN CONTAINS 42000 RECORDS WHERE AS TARGET Y_TRAIN HAS 42000 ELEMENTS
* X_TEST CONTAINS 18000 RECORDS WHERE AS Y_TEST CONTAINS 18000 ELEMENTS.
* SINCE THERE SAME NUMBER OF PREDICTORS AS TARGET VARIABLES WE CAN SAY THAT THE DATA IS IN SYNC
for i in range(0,10):
plt.subplot(1,10,i+1)
plt.imshow(X_TRAIN[i])
plt.axis('off')
plt.show()
print('Label: ', Y_TRAIN[:10])
Label: [2 6 7 4 4 0 3 0 7 3]
* WE HAVE DISPLAYED THE FIRST 10 IMAGES FROM THE X_TRAIN AND CORRESPONDING Y_LABLES
* SHAPE OF THE TRAINING IMAGES IS IN 3 DIMENSIONAL.
* WE WILL CHANGE THE IMAGES DATA FROM 3 DIMENSIONAL TO 2 DIMENSIONAL.
X_TRAIN = np.asarray(X_TRAIN).reshape(42000,1024)
X_TEST = np.asarray(X_TEST).reshape(18000,1024)
X_VAL = np.asarray(X_VAL).reshape(60000,1024)
print('SHAPE OF X_TRAIN: ',X_TRAIN.shape)
print('\nSHAPE OF X_TEST: ',X_TEST.shape)
print('\nSHAPE OF X_VAL: ',X_VAL.shape)
SHAPE OF X_TRAIN: (42000, 1024) SHAPE OF X_TEST: (18000, 1024) SHAPE OF X_VAL: (60000, 1024)
* WE HAVE CHANGED THE DIMNESIONS TO 2 DIMENSIONAL AS BELOW:
(60000,32,32) --- (60000,1024)
(42000,32,32) --- (42000,1024)
(18000,32,32) --- (18000,1024)
* IN ORDER TO NORMALIZE THE PIXEL VALUES, WE WILL DIVIDE THEM BY 255 WHICH IS THE MAX RGB VALUE
* CONVERT THE NORMALIZED PIXEL VALUES INTO FLOATING DATA TYPE
X_TRAIN /= 255
X_VAL /= 255
X_TEST /= 255
X_TRAIN = X_TRAIN.astype('float32')
X_VAL = X_VAL.astype('float32')
X_TEST = X_TEST.astype('float32')
X_TRAIN[:5]
array([[0.12968785, 0.11866706, 0.10530196, ..., 0.19477727, 0.19942354,
0.20799099],
[0.34101608, 0.3414451 , 0.34656274, ..., 0.29498273, 0.30054745,
0.31092745],
[0.48284313, 0.49356118, 0.47872823, ..., 0.36837214, 0.41093138,
0.44637334],
[0.5789004 , 0.547531 , 0.5592945 , ..., 0.6161545 , 0.6163902 ,
0.6163902 ],
[0.60387844, 0.6085423 , 0.61736315, ..., 0.17918824, 0.19065568,
0.22803685]], dtype=float32)
* WE CHANGE THE FORMAT OF THE EACH LABEL INTO ARRAY FORMAT WHERE THE SIZE IS THE TOTAL UNIQUE ELEMENTS.
print('UNIQUE VALUES IN TRAINING DATA:',np.unique(Y_TRAIN))
print('\nUNIQUE VALUES IN TESTING DATA:',np.unique(Y_TEST))
print('\nUNIQUE VALUES IN VALIDATION DATA:',np.unique(Y_VAL))
UNIQUE VALUES IN TRAINING DATA: [0 1 2 3 4 5 6 7 8 9] UNIQUE VALUES IN TESTING DATA: [0 1 2 3 4 5 6 7 8 9] UNIQUE VALUES IN VALIDATION DATA: [0 1 2 3 4 5 6 7 8 9]
Y_TRAIN = to_categorical(Y_TRAIN, num_classes=10)
Y_TEST = to_categorical(Y_TEST, num_classes=10)
Y_VAL = to_categorical(Y_VAL, num_classes=10)
print('SHAPE OF Y_TRAIN: ',Y_TRAIN.shape)
print('\nSHAPE OF Y_TEST: ',Y_TEST.shape)
print('\nSHAPE OF Y_VAL: ',Y_VAL.shape)
SHAPE OF Y_TRAIN: (42000, 10) SHAPE OF Y_TEST: (18000, 10) SHAPE OF Y_VAL: (60000, 10)
print('NO OF CLASSES IN THE DATASET: ',len(set(SVHN_DATA['y_train'])))
print('\n',np.unique(SVHN_DATA['y_train']))
NO OF CLASSES IN THE DATASET: 10 [0 1 2 3 4 5 6 7 8 9]
* THERE ARE TOTAL OF 10 CLASSES IN THE DATASET RANGING FROM 0 TO 9.
MODEL = Sequential()
MODEL.add(Dense(1024, kernel_initializer='normal',input_shape = (1024, )))
MODEL.add(Activation('relu'))
MODEL.add(Dense(256, kernel_initializer='normal'))
MODEL.add(Activation('relu'))
MODEL.add(Dense(512, kernel_initializer='normal'))
MODEL.add(Activation('relu'))
MODEL.add(Dense(256, kernel_initializer='normal'))
MODEL.add(Activation('relu'))
MODEL.add(Dense(128, kernel_initializer='normal'))
MODEL.add(Activation('relu'))
MODEL.add(Dense(64, kernel_initializer='normal'))
MODEL.add(Activation('relu'))
MODEL.add(Dense(128, kernel_initializer='normal'))
MODEL.add(LeakyReLU(alpha=0.1))
MODEL.add(Dense(128, kernel_initializer='normal'))
MODEL.add(LeakyReLU(alpha=0.1))
MODEL.add(Dense(128, kernel_initializer='normal'))
MODEL.add(LeakyReLU(alpha=0.1))
MODEL.add(Dense(10))
MODEL.add(Activation('softmax'))
MODEL.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY = MODEL.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),batch_size = 200, epochs = 100, verbose = 1)
Epoch 1/100 210/210 [==============================] - 13s 60ms/step - loss: 0.8674 - accuracy: 0.7182 - val_loss: 1.0145 - val_accuracy: 0.6762 Epoch 2/100 210/210 [==============================] - 13s 61ms/step - loss: 0.8840 - accuracy: 0.7119 - val_loss: 0.9825 - val_accuracy: 0.6950 Epoch 3/100 210/210 [==============================] - 15s 73ms/step - loss: 0.8239 - accuracy: 0.7333 - val_loss: 0.9487 - val_accuracy: 0.7132 Epoch 4/100 210/210 [==============================] - 15s 71ms/step - loss: 0.8091 - accuracy: 0.7387 - val_loss: 0.9774 - val_accuracy: 0.6922 Epoch 5/100 210/210 [==============================] - 16s 77ms/step - loss: 0.7916 - accuracy: 0.7440 - val_loss: 0.9497 - val_accuracy: 0.7042 Epoch 6/100 210/210 [==============================] - 17s 81ms/step - loss: 0.8267 - accuracy: 0.7358 - val_loss: 0.9232 - val_accuracy: 0.7138 Epoch 7/100 210/210 [==============================] - 17s 80ms/step - loss: 0.7497 - accuracy: 0.7579 - val_loss: 0.9248 - val_accuracy: 0.7201 Epoch 8/100 210/210 [==============================] - 17s 81ms/step - loss: 0.7491 - accuracy: 0.7560 - val_loss: 0.9098 - val_accuracy: 0.7183 Epoch 9/100 210/210 [==============================] - 17s 80ms/step - loss: 0.7266 - accuracy: 0.7615 - val_loss: 0.9107 - val_accuracy: 0.7177 Epoch 10/100 210/210 [==============================] - 17s 81ms/step - loss: 0.7179 - accuracy: 0.7620 - val_loss: 0.8447 - val_accuracy: 0.7399 Epoch 11/100 210/210 [==============================] - 16s 78ms/step - loss: 0.7026 - accuracy: 0.7708 - val_loss: 0.9103 - val_accuracy: 0.7123 Epoch 12/100 210/210 [==============================] - 17s 82ms/step - loss: 0.6905 - accuracy: 0.7734 - val_loss: 0.9468 - val_accuracy: 0.6999 Epoch 13/100 210/210 [==============================] - 17s 82ms/step - loss: 0.6797 - accuracy: 0.7766 - val_loss: 1.1295 - val_accuracy: 0.6418 Epoch 14/100 210/210 [==============================] - 17s 83ms/step - loss: 0.6486 - accuracy: 0.7866 - val_loss: 0.9229 - val_accuracy: 0.7134 Epoch 15/100 210/210 [==============================] - 18s 83ms/step - loss: 0.6335 - accuracy: 0.7924 - val_loss: 0.8648 - val_accuracy: 0.7292 Epoch 16/100 210/210 [==============================] - 18s 85ms/step - loss: 0.6205 - accuracy: 0.7957 - val_loss: 0.8616 - val_accuracy: 0.7337 Epoch 17/100 210/210 [==============================] - 17s 83ms/step - loss: 0.6182 - accuracy: 0.7961 - val_loss: 0.8124 - val_accuracy: 0.7541 Epoch 18/100 210/210 [==============================] - 18s 84ms/step - loss: 0.6159 - accuracy: 0.7965 - val_loss: 0.8793 - val_accuracy: 0.7407 Epoch 19/100 210/210 [==============================] - 18s 84ms/step - loss: 0.6034 - accuracy: 0.8015 - val_loss: 0.8517 - val_accuracy: 0.7437 Epoch 20/100 210/210 [==============================] - 17s 83ms/step - loss: 0.5907 - accuracy: 0.8063 - val_loss: 0.8515 - val_accuracy: 0.7394 Epoch 21/100 210/210 [==============================] - 17s 82ms/step - loss: 0.5931 - accuracy: 0.8021 - val_loss: 0.8117 - val_accuracy: 0.7562 Epoch 22/100 210/210 [==============================] - 17s 82ms/step - loss: 0.5776 - accuracy: 0.8104 - val_loss: 0.8297 - val_accuracy: 0.7490 Epoch 23/100 210/210 [==============================] - 17s 82ms/step - loss: 0.5531 - accuracy: 0.8175 - val_loss: 0.8406 - val_accuracy: 0.7489 Epoch 24/100 210/210 [==============================] - 17s 83ms/step - loss: 0.5699 - accuracy: 0.8115 - val_loss: 0.8510 - val_accuracy: 0.7433 Epoch 25/100 210/210 [==============================] - 17s 82ms/step - loss: 0.7715 - accuracy: 0.7636 - val_loss: 0.7769 - val_accuracy: 0.7658 Epoch 26/100 210/210 [==============================] - 20s 94ms/step - loss: 0.5297 - accuracy: 0.8271 - val_loss: 0.7910 - val_accuracy: 0.7665 Epoch 27/100 210/210 [==============================] - 17s 80ms/step - loss: 0.5173 - accuracy: 0.8316 - val_loss: 0.8667 - val_accuracy: 0.7353 Epoch 28/100 210/210 [==============================] - 16s 76ms/step - loss: 0.5331 - accuracy: 0.8246 - val_loss: 0.8390 - val_accuracy: 0.7483 Epoch 29/100 210/210 [==============================] - 17s 80ms/step - loss: 0.5072 - accuracy: 0.8341 - val_loss: 0.9043 - val_accuracy: 0.7262 Epoch 30/100 210/210 [==============================] - 18s 83ms/step - loss: 0.4913 - accuracy: 0.8386 - val_loss: 0.8185 - val_accuracy: 0.7553 Epoch 31/100 210/210 [==============================] - 17s 82ms/step - loss: 0.5255 - accuracy: 0.8285 - val_loss: 0.7658 - val_accuracy: 0.7725 Epoch 32/100 210/210 [==============================] - 17s 80ms/step - loss: 0.4853 - accuracy: 0.8407 - val_loss: 0.8576 - val_accuracy: 0.7418 Epoch 33/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4953 - accuracy: 0.8376 - val_loss: 0.7994 - val_accuracy: 0.7662 Epoch 34/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4959 - accuracy: 0.8380 - val_loss: 0.8688 - val_accuracy: 0.7478 Epoch 35/100 210/210 [==============================] - 17s 82ms/step - loss: 0.4615 - accuracy: 0.8493 - val_loss: 0.7909 - val_accuracy: 0.7698 Epoch 36/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4650 - accuracy: 0.8464 - val_loss: 0.8871 - val_accuracy: 0.7393 Epoch 37/100 210/210 [==============================] - 18s 86ms/step - loss: 0.4650 - accuracy: 0.8468 - val_loss: 0.8112 - val_accuracy: 0.7637 Epoch 38/100 210/210 [==============================] - 18s 85ms/step - loss: 0.4598 - accuracy: 0.8499 - val_loss: 0.9397 - val_accuracy: 0.7423 Epoch 39/100 210/210 [==============================] - 17s 82ms/step - loss: 0.5688 - accuracy: 0.8223 - val_loss: 0.7804 - val_accuracy: 0.7787 Epoch 40/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4587 - accuracy: 0.8498 - val_loss: 0.7579 - val_accuracy: 0.7798 Epoch 41/100 210/210 [==============================] - 17s 82ms/step - loss: 0.4109 - accuracy: 0.8669 - val_loss: 0.8023 - val_accuracy: 0.7721 Epoch 42/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4411 - accuracy: 0.8538 - val_loss: 0.7849 - val_accuracy: 0.7775 Epoch 43/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4117 - accuracy: 0.8664 - val_loss: 0.7937 - val_accuracy: 0.7767 Epoch 44/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4167 - accuracy: 0.8628 - val_loss: 0.8020 - val_accuracy: 0.7749 Epoch 45/100 210/210 [==============================] - 17s 82ms/step - loss: 0.4030 - accuracy: 0.8696 - val_loss: 0.8070 - val_accuracy: 0.7647 Epoch 46/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4284 - accuracy: 0.8590 - val_loss: 0.7945 - val_accuracy: 0.7712 Epoch 47/100 210/210 [==============================] - 17s 81ms/step - loss: 0.3866 - accuracy: 0.8757 - val_loss: 0.8873 - val_accuracy: 0.7552 Epoch 48/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4149 - accuracy: 0.8648 - val_loss: 0.8254 - val_accuracy: 0.7702 Epoch 49/100 210/210 [==============================] - 17s 81ms/step - loss: 0.3835 - accuracy: 0.8747 - val_loss: 0.8128 - val_accuracy: 0.7703 Epoch 50/100 210/210 [==============================] - 18s 87ms/step - loss: 0.3750 - accuracy: 0.8768 - val_loss: 0.7991 - val_accuracy: 0.7853 Epoch 51/100 210/210 [==============================] - 17s 82ms/step - loss: 0.3901 - accuracy: 0.8725 - val_loss: 0.8226 - val_accuracy: 0.7745 Epoch 52/100 210/210 [==============================] - 18s 84ms/step - loss: 0.3901 - accuracy: 0.8729 - val_loss: 0.8147 - val_accuracy: 0.7787 Epoch 53/100 210/210 [==============================] - 17s 83ms/step - loss: 0.3527 - accuracy: 0.8842 - val_loss: 0.9427 - val_accuracy: 0.7653 Epoch 54/100 210/210 [==============================] - 17s 82ms/step - loss: 0.3566 - accuracy: 0.8839 - val_loss: 0.7980 - val_accuracy: 0.7824 Epoch 55/100 210/210 [==============================] - 17s 82ms/step - loss: 0.3492 - accuracy: 0.8868 - val_loss: 0.8145 - val_accuracy: 0.7892 Epoch 56/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4929 - accuracy: 0.8488 - val_loss: 0.7886 - val_accuracy: 0.7871 Epoch 57/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3350 - accuracy: 0.8929 - val_loss: 0.8096 - val_accuracy: 0.7847 Epoch 58/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3248 - accuracy: 0.8949 - val_loss: 0.8692 - val_accuracy: 0.7760 Epoch 59/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3185 - accuracy: 0.8985 - val_loss: 0.8586 - val_accuracy: 0.7625 Epoch 60/100 210/210 [==============================] - 17s 80ms/step - loss: 0.5159 - accuracy: 0.8456 - val_loss: 0.8047 - val_accuracy: 0.7576 Epoch 61/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3596 - accuracy: 0.8839 - val_loss: 0.8283 - val_accuracy: 0.7760 Epoch 62/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3134 - accuracy: 0.8983 - val_loss: 0.8043 - val_accuracy: 0.7887 Epoch 63/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3180 - accuracy: 0.8961 - val_loss: 0.8238 - val_accuracy: 0.7869 Epoch 64/100 210/210 [==============================] - 17s 80ms/step - loss: 0.4610 - accuracy: 0.8640 - val_loss: 0.7656 - val_accuracy: 0.7805 Epoch 65/100 210/210 [==============================] - 18s 86ms/step - loss: 0.3014 - accuracy: 0.9033 - val_loss: 0.8159 - val_accuracy: 0.7852 Epoch 66/100 210/210 [==============================] - 17s 80ms/step - loss: 0.3030 - accuracy: 0.9015 - val_loss: 0.8817 - val_accuracy: 0.7823 Epoch 67/100 210/210 [==============================] - 17s 80ms/step - loss: 0.2935 - accuracy: 0.9049 - val_loss: 0.8405 - val_accuracy: 0.7874 Epoch 68/100 210/210 [==============================] - 17s 80ms/step - loss: 0.2987 - accuracy: 0.9036 - val_loss: 0.8343 - val_accuracy: 0.7923 Epoch 69/100 210/210 [==============================] - 17s 80ms/step - loss: 0.2910 - accuracy: 0.9060 - val_loss: 0.8053 - val_accuracy: 0.7960 Epoch 70/100 210/210 [==============================] - 17s 81ms/step - loss: 0.3197 - accuracy: 0.8947 - val_loss: 0.9322 - val_accuracy: 0.7552 Epoch 71/100 210/210 [==============================] - 17s 80ms/step - loss: 0.2844 - accuracy: 0.9084 - val_loss: 0.8617 - val_accuracy: 0.7696 Epoch 72/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2805 - accuracy: 0.9076 - val_loss: 0.8540 - val_accuracy: 0.7846 Epoch 73/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2832 - accuracy: 0.9059 - val_loss: 1.1363 - val_accuracy: 0.7334 Epoch 74/100 210/210 [==============================] - 17s 83ms/step - loss: 0.2962 - accuracy: 0.9046 - val_loss: 0.8376 - val_accuracy: 0.7951 Epoch 75/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2626 - accuracy: 0.9139 - val_loss: 0.9078 - val_accuracy: 0.7798 Epoch 76/100 210/210 [==============================] - 17s 81ms/step - loss: 0.3084 - accuracy: 0.9033 - val_loss: 0.9048 - val_accuracy: 0.7908 Epoch 77/100 210/210 [==============================] - 18s 85ms/step - loss: 0.2573 - accuracy: 0.9170 - val_loss: 0.9005 - val_accuracy: 0.7909 Epoch 78/100 210/210 [==============================] - 18s 88ms/step - loss: 0.2781 - accuracy: 0.9086 - val_loss: 0.9142 - val_accuracy: 0.7504 Epoch 79/100 210/210 [==============================] - 18s 85ms/step - loss: 0.2486 - accuracy: 0.9186 - val_loss: 0.8673 - val_accuracy: 0.7937 Epoch 80/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2648 - accuracy: 0.9131 - val_loss: 1.0257 - val_accuracy: 0.7555 Epoch 81/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2508 - accuracy: 0.9171 - val_loss: 0.8428 - val_accuracy: 0.7953 Epoch 82/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2211 - accuracy: 0.9302 - val_loss: 1.0973 - val_accuracy: 0.7464 Epoch 83/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2719 - accuracy: 0.9131 - val_loss: 0.9211 - val_accuracy: 0.7922 Epoch 84/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2292 - accuracy: 0.9260 - val_loss: 1.2131 - val_accuracy: 0.7484 Epoch 85/100 210/210 [==============================] - 17s 83ms/step - loss: 0.2304 - accuracy: 0.9248 - val_loss: 0.8958 - val_accuracy: 0.7953 Epoch 86/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2348 - accuracy: 0.9245 - val_loss: 0.8942 - val_accuracy: 0.7929 Epoch 87/100 210/210 [==============================] - 17s 83ms/step - loss: 0.2263 - accuracy: 0.9263 - val_loss: 0.9022 - val_accuracy: 0.7981 Epoch 88/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2553 - accuracy: 0.9172 - val_loss: 0.8738 - val_accuracy: 0.7980 Epoch 89/100 210/210 [==============================] - 17s 82ms/step - loss: 0.2448 - accuracy: 0.9217 - val_loss: 1.0763 - val_accuracy: 0.7504 Epoch 90/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2133 - accuracy: 0.9318 - val_loss: 0.9485 - val_accuracy: 0.7822 Epoch 91/100 210/210 [==============================] - 17s 81ms/step - loss: 0.4104 - accuracy: 0.8838 - val_loss: 0.8578 - val_accuracy: 0.7653 Epoch 92/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2245 - accuracy: 0.9275 - val_loss: 0.8755 - val_accuracy: 0.7930 Epoch 93/100 210/210 [==============================] - 17s 81ms/step - loss: 0.2025 - accuracy: 0.9353 - val_loss: 0.8941 - val_accuracy: 0.7928 Epoch 94/100 210/210 [==============================] - 18s 87ms/step - loss: 0.1945 - accuracy: 0.9377 - val_loss: 1.0652 - val_accuracy: 0.7718 Epoch 95/100 210/210 [==============================] - 17s 81ms/step - loss: 0.1944 - accuracy: 0.9377 - val_loss: 0.9994 - val_accuracy: 0.7914 Epoch 96/100 210/210 [==============================] - 17s 81ms/step - loss: 0.1863 - accuracy: 0.9406 - val_loss: 0.9393 - val_accuracy: 0.8038 Epoch 97/100 210/210 [==============================] - 17s 83ms/step - loss: 0.2981 - accuracy: 0.9126 - val_loss: 0.8887 - val_accuracy: 0.8027 Epoch 98/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1775 - accuracy: 0.9445 - val_loss: 0.9966 - val_accuracy: 0.7847 Epoch 99/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2162 - accuracy: 0.9328 - val_loss: 0.9397 - val_accuracy: 0.7903 Epoch 100/100 210/210 [==============================] - 19s 92ms/step - loss: 0.1911 - accuracy: 0.9402 - val_loss: 0.9481 - val_accuracy: 0.7932
MODEL_DF = pd.DataFrame.from_dict(HISTORY.history)
Y_PRED = MODEL.predict(X_TEST)
print('MODEL SCORE: ', metrics.r2_score(Y_TEST,Y_PRED))
print('\n\n',MODEL_DF.sort_values('accuracy',ascending = False).head())
563/563 [==============================] - 6s 10ms/step
MODEL SCORE: 0.6557633973226642
loss accuracy val_loss val_accuracy
97 0.177493 0.944548 0.996580 0.784722
95 0.186299 0.940595 0.939283 0.803778
99 0.191058 0.940214 0.948085 0.793222
93 0.194492 0.937738 1.065166 0.771833
94 0.194438 0.937690 0.999405 0.791389
# TRY THE DESIGN WITH VARIOUS OPTIMIZERS AND COMPILE
OPT = ['Adam', 'RMSprop', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl']
RESULTS = pd.DataFrame()
for i in OPT:
MODEL.compile(loss='categorical_crossentropy', optimizer=i, metrics=['accuracy'])
OPT_HIST = MODEL.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),epochs=100, batch_size=200).history
Y_PRED = MODEL.predict(X_TEST)
SCORE = metrics.r2_score(Y_TEST,Y_PRED)
MAX_ACC = np.max(OPT_HIST['accuracy'])
MAX_VAL_ACC = np.max(OPT_HIST['val_accuracy'])
RESULTS = RESULTS.append(pd.Series([i,SCORE,MAX_ACC,MAX_VAL_ACC]),ignore_index=True)
RESULTS.columns = ['NO OF EPOCHS','MODEL SCORE','TRAINING ACCURACY','TESTING ACCURACY']
Epoch 1/100 210/210 [==============================] - 16s 71ms/step - loss: 1.9581 - accuracy: 0.3350 - val_loss: 1.4735 - val_accuracy: 0.4776 Epoch 2/100 210/210 [==============================] - 12s 59ms/step - loss: 1.2707 - accuracy: 0.5670 - val_loss: 1.2108 - val_accuracy: 0.5953 Epoch 3/100 210/210 [==============================] - 16s 78ms/step - loss: 1.0645 - accuracy: 0.6451 - val_loss: 0.9822 - val_accuracy: 0.6881 Epoch 4/100 210/210 [==============================] - 16s 77ms/step - loss: 0.9252 - accuracy: 0.6994 - val_loss: 0.9653 - val_accuracy: 0.6917 Epoch 5/100 210/210 [==============================] - 16s 78ms/step - loss: 0.8577 - accuracy: 0.7217 - val_loss: 0.9489 - val_accuracy: 0.7042 Epoch 6/100 210/210 [==============================] - 16s 78ms/step - loss: 0.7855 - accuracy: 0.7482 - val_loss: 0.8135 - val_accuracy: 0.7450 Epoch 7/100 210/210 [==============================] - 18s 84ms/step - loss: 0.7670 - accuracy: 0.7564 - val_loss: 0.7740 - val_accuracy: 0.7620 Epoch 8/100 210/210 [==============================] - 17s 83ms/step - loss: 0.7413 - accuracy: 0.7631 - val_loss: 0.7945 - val_accuracy: 0.7550 Epoch 9/100 210/210 [==============================] - 17s 82ms/step - loss: 0.6987 - accuracy: 0.7788 - val_loss: 0.7239 - val_accuracy: 0.7793 Epoch 10/100 210/210 [==============================] - 18s 84ms/step - loss: 0.6585 - accuracy: 0.7917 - val_loss: 0.6901 - val_accuracy: 0.7859 Epoch 11/100 210/210 [==============================] - 19s 91ms/step - loss: 0.6559 - accuracy: 0.7915 - val_loss: 0.7224 - val_accuracy: 0.7800 Epoch 12/100 210/210 [==============================] - 18s 83ms/step - loss: 0.6303 - accuracy: 0.8010 - val_loss: 0.7620 - val_accuracy: 0.7604 Epoch 13/100 210/210 [==============================] - 19s 90ms/step - loss: 0.5989 - accuracy: 0.8117 - val_loss: 0.7042 - val_accuracy: 0.7878 Epoch 14/100 210/210 [==============================] - 18s 85ms/step - loss: 0.5905 - accuracy: 0.8158 - val_loss: 0.6653 - val_accuracy: 0.7937 Epoch 15/100 210/210 [==============================] - 19s 91ms/step - loss: 0.5640 - accuracy: 0.8203 - val_loss: 0.7275 - val_accuracy: 0.7790 Epoch 16/100 210/210 [==============================] - 18s 86ms/step - loss: 0.5387 - accuracy: 0.8322 - val_loss: 0.6356 - val_accuracy: 0.8122 Epoch 17/100 210/210 [==============================] - 18s 85ms/step - loss: 0.5315 - accuracy: 0.8329 - val_loss: 0.6272 - val_accuracy: 0.8161 Epoch 18/100 210/210 [==============================] - 18s 86ms/step - loss: 0.5156 - accuracy: 0.8376 - val_loss: 0.6452 - val_accuracy: 0.8106 Epoch 19/100 210/210 [==============================] - 18s 86ms/step - loss: 0.4955 - accuracy: 0.8433 - val_loss: 0.6604 - val_accuracy: 0.8095 Epoch 20/100 210/210 [==============================] - 18s 85ms/step - loss: 0.4992 - accuracy: 0.8430 - val_loss: 0.6821 - val_accuracy: 0.7991 Epoch 21/100 210/210 [==============================] - 18s 85ms/step - loss: 0.4721 - accuracy: 0.8514 - val_loss: 0.6348 - val_accuracy: 0.8122 Epoch 22/100 210/210 [==============================] - 18s 86ms/step - loss: 0.4599 - accuracy: 0.8537 - val_loss: 0.7316 - val_accuracy: 0.7786 Epoch 23/100 210/210 [==============================] - 18s 86ms/step - loss: 0.4431 - accuracy: 0.8605 - val_loss: 0.6057 - val_accuracy: 0.8237 Epoch 24/100 210/210 [==============================] - 18s 85ms/step - loss: 0.4251 - accuracy: 0.8647 - val_loss: 0.6989 - val_accuracy: 0.8000 Epoch 25/100 210/210 [==============================] - 18s 85ms/step - loss: 0.4102 - accuracy: 0.8702 - val_loss: 0.6218 - val_accuracy: 0.8208 Epoch 26/100 210/210 [==============================] - 18s 86ms/step - loss: 0.3973 - accuracy: 0.8743 - val_loss: 0.6262 - val_accuracy: 0.8202 Epoch 27/100 210/210 [==============================] - 18s 85ms/step - loss: 0.3809 - accuracy: 0.8780 - val_loss: 0.6579 - val_accuracy: 0.8166 Epoch 28/100 210/210 [==============================] - 18s 86ms/step - loss: 0.3843 - accuracy: 0.8798 - val_loss: 0.6572 - val_accuracy: 0.8204 Epoch 29/100 210/210 [==============================] - 18s 85ms/step - loss: 0.3704 - accuracy: 0.8826 - val_loss: 0.6724 - val_accuracy: 0.8129 Epoch 30/100 210/210 [==============================] - 18s 88ms/step - loss: 0.3658 - accuracy: 0.8822 - val_loss: 0.6762 - val_accuracy: 0.8067 Epoch 31/100 210/210 [==============================] - 18s 87ms/step - loss: 0.3531 - accuracy: 0.8864 - val_loss: 0.5956 - val_accuracy: 0.8321 Epoch 32/100 210/210 [==============================] - 18s 86ms/step - loss: 0.3265 - accuracy: 0.8953 - val_loss: 0.6417 - val_accuracy: 0.8277 Epoch 33/100 210/210 [==============================] - 18s 86ms/step - loss: 0.3212 - accuracy: 0.8980 - val_loss: 0.6667 - val_accuracy: 0.8197 Epoch 34/100 210/210 [==============================] - 18s 85ms/step - loss: 0.3023 - accuracy: 0.9027 - val_loss: 0.6836 - val_accuracy: 0.8186 Epoch 35/100 210/210 [==============================] - 18s 85ms/step - loss: 0.3055 - accuracy: 0.8998 - val_loss: 0.6818 - val_accuracy: 0.8159 Epoch 36/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2898 - accuracy: 0.9066 - val_loss: 0.7228 - val_accuracy: 0.8152 Epoch 37/100 210/210 [==============================] - 19s 88ms/step - loss: 0.2971 - accuracy: 0.9042 - val_loss: 0.7319 - val_accuracy: 0.8119 Epoch 38/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2888 - accuracy: 0.9067 - val_loss: 0.6675 - val_accuracy: 0.8257 Epoch 39/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2731 - accuracy: 0.9125 - val_loss: 0.6484 - val_accuracy: 0.8318 Epoch 40/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2731 - accuracy: 0.9113 - val_loss: 0.7163 - val_accuracy: 0.8221 Epoch 41/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2525 - accuracy: 0.9196 - val_loss: 0.7747 - val_accuracy: 0.8183 Epoch 42/100 210/210 [==============================] - 19s 89ms/step - loss: 0.2526 - accuracy: 0.9171 - val_loss: 0.6920 - val_accuracy: 0.8309 Epoch 43/100 210/210 [==============================] - 19s 88ms/step - loss: 0.2336 - accuracy: 0.9238 - val_loss: 0.7606 - val_accuracy: 0.8107 Epoch 44/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2529 - accuracy: 0.9182 - val_loss: 0.7813 - val_accuracy: 0.8111 Epoch 45/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2253 - accuracy: 0.9265 - val_loss: 0.7224 - val_accuracy: 0.8296 Epoch 46/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2049 - accuracy: 0.9339 - val_loss: 0.8319 - val_accuracy: 0.8152 Epoch 47/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2195 - accuracy: 0.9276 - val_loss: 0.7142 - val_accuracy: 0.8314 Epoch 48/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2151 - accuracy: 0.9304 - val_loss: 0.7163 - val_accuracy: 0.8334 Epoch 49/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1920 - accuracy: 0.9376 - val_loss: 0.7114 - val_accuracy: 0.8323 Epoch 50/100 210/210 [==============================] - 18s 87ms/step - loss: 0.2101 - accuracy: 0.9311 - val_loss: 0.7777 - val_accuracy: 0.8119 Epoch 51/100 210/210 [==============================] - 18s 86ms/step - loss: 0.2142 - accuracy: 0.9292 - val_loss: 0.7531 - val_accuracy: 0.8313 Epoch 52/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1861 - accuracy: 0.9392 - val_loss: 0.7586 - val_accuracy: 0.8349 Epoch 53/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1926 - accuracy: 0.9377 - val_loss: 0.8562 - val_accuracy: 0.8216 Epoch 54/100 210/210 [==============================] - 18s 87ms/step - loss: 0.1843 - accuracy: 0.9400 - val_loss: 0.7802 - val_accuracy: 0.8327 Epoch 55/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1906 - accuracy: 0.9371 - val_loss: 0.8025 - val_accuracy: 0.8148 Epoch 56/100 210/210 [==============================] - 18s 86ms/step - loss: 0.1825 - accuracy: 0.9405 - val_loss: 0.7494 - val_accuracy: 0.8205 Epoch 57/100 210/210 [==============================] - 17s 83ms/step - loss: 0.1540 - accuracy: 0.9498 - val_loss: 0.7761 - val_accuracy: 0.8394 Epoch 58/100 210/210 [==============================] - 17s 83ms/step - loss: 0.1667 - accuracy: 0.9458 - val_loss: 0.8114 - val_accuracy: 0.8301 Epoch 59/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1608 - accuracy: 0.9481 - val_loss: 0.8100 - val_accuracy: 0.8305 Epoch 60/100 210/210 [==============================] - 17s 83ms/step - loss: 0.1579 - accuracy: 0.9487 - val_loss: 0.7963 - val_accuracy: 0.8245 Epoch 61/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1741 - accuracy: 0.9430 - val_loss: 0.8275 - val_accuracy: 0.8336 Epoch 62/100 210/210 [==============================] - 17s 83ms/step - loss: 0.1749 - accuracy: 0.9433 - val_loss: 0.8403 - val_accuracy: 0.8265 Epoch 63/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1438 - accuracy: 0.9532 - val_loss: 0.8152 - val_accuracy: 0.8202 Epoch 64/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1404 - accuracy: 0.9539 - val_loss: 0.8652 - val_accuracy: 0.8243 Epoch 65/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1359 - accuracy: 0.9556 - val_loss: 0.8618 - val_accuracy: 0.8228 Epoch 66/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1402 - accuracy: 0.9540 - val_loss: 0.9907 - val_accuracy: 0.8270 Epoch 67/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1550 - accuracy: 0.9488 - val_loss: 0.8207 - val_accuracy: 0.8374 Epoch 68/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1312 - accuracy: 0.9574 - val_loss: 0.9202 - val_accuracy: 0.8248 Epoch 69/100 210/210 [==============================] - 18s 85ms/step - loss: 0.1349 - accuracy: 0.9561 - val_loss: 0.8701 - val_accuracy: 0.8280 Epoch 70/100 210/210 [==============================] - 18s 85ms/step - loss: 0.1214 - accuracy: 0.9595 - val_loss: 0.8727 - val_accuracy: 0.8323 Epoch 71/100 210/210 [==============================] - 17s 83ms/step - loss: 0.1444 - accuracy: 0.9520 - val_loss: 0.8630 - val_accuracy: 0.8308 Epoch 72/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1154 - accuracy: 0.9624 - val_loss: 0.8864 - val_accuracy: 0.8270 Epoch 73/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1226 - accuracy: 0.9594 - val_loss: 0.9629 - val_accuracy: 0.8073 Epoch 74/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1280 - accuracy: 0.9573 - val_loss: 1.0345 - val_accuracy: 0.8156 Epoch 75/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1270 - accuracy: 0.9589 - val_loss: 0.8357 - val_accuracy: 0.8271 Epoch 76/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1278 - accuracy: 0.9590 - val_loss: 0.8903 - val_accuracy: 0.8292 Epoch 77/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1166 - accuracy: 0.9619 - val_loss: 0.9315 - val_accuracy: 0.8228 Epoch 78/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1216 - accuracy: 0.9603 - val_loss: 0.8832 - val_accuracy: 0.8304 Epoch 79/100 210/210 [==============================] - 18s 85ms/step - loss: 0.1246 - accuracy: 0.9592 - val_loss: 0.8736 - val_accuracy: 0.8364 Epoch 80/100 210/210 [==============================] - 18s 85ms/step - loss: 0.1038 - accuracy: 0.9659 - val_loss: 1.0018 - val_accuracy: 0.8123 Epoch 81/100 210/210 [==============================] - 18s 85ms/step - loss: 0.1196 - accuracy: 0.9615 - val_loss: 0.9121 - val_accuracy: 0.8299 Epoch 82/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1082 - accuracy: 0.9650 - val_loss: 0.9387 - val_accuracy: 0.8387 Epoch 83/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.9223 - val_accuracy: 0.8186 Epoch 84/100 210/210 [==============================] - 18s 83ms/step - loss: 0.1111 - accuracy: 0.9634 - val_loss: 0.9276 - val_accuracy: 0.8328 Epoch 85/100 210/210 [==============================] - 18s 84ms/step - loss: 0.0981 - accuracy: 0.9679 - val_loss: 0.8706 - val_accuracy: 0.8359 Epoch 86/100 210/210 [==============================] - 18s 84ms/step - loss: 0.1041 - accuracy: 0.9657 - val_loss: 1.0142 - val_accuracy: 0.8249 Epoch 87/100 210/210 [==============================] - 19s 89ms/step - loss: 0.1060 - accuracy: 0.9649 - val_loss: 0.9418 - val_accuracy: 0.8220 Epoch 88/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0992 - accuracy: 0.9673 - val_loss: 0.9224 - val_accuracy: 0.8319 Epoch 89/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0957 - accuracy: 0.9691 - val_loss: 0.9315 - val_accuracy: 0.8292 Epoch 90/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0982 - accuracy: 0.9679 - val_loss: 0.8754 - val_accuracy: 0.8272 Epoch 91/100 210/210 [==============================] - 18s 85ms/step - loss: 0.0900 - accuracy: 0.9705 - val_loss: 1.0087 - val_accuracy: 0.8342 Epoch 92/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.9663 - val_accuracy: 0.8246 Epoch 93/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0835 - accuracy: 0.9725 - val_loss: 0.9797 - val_accuracy: 0.8240 Epoch 94/100 210/210 [==============================] - 18s 84ms/step - loss: 0.0936 - accuracy: 0.9694 - val_loss: 0.9227 - val_accuracy: 0.8251 Epoch 95/100 210/210 [==============================] - 17s 79ms/step - loss: 0.0923 - accuracy: 0.9704 - val_loss: 0.9712 - val_accuracy: 0.8191 Epoch 96/100 210/210 [==============================] - 16s 76ms/step - loss: 0.1019 - accuracy: 0.9672 - val_loss: 0.7877 - val_accuracy: 0.8389 Epoch 97/100 210/210 [==============================] - 18s 85ms/step - loss: 0.0931 - accuracy: 0.9705 - val_loss: 1.0438 - val_accuracy: 0.8356 Epoch 98/100 210/210 [==============================] - 16s 77ms/step - loss: 0.0925 - accuracy: 0.9705 - val_loss: 0.9362 - val_accuracy: 0.8304 Epoch 99/100 210/210 [==============================] - 16s 76ms/step - loss: 0.0842 - accuracy: 0.9733 - val_loss: 0.8735 - val_accuracy: 0.8180 Epoch 100/100 210/210 [==============================] - 16s 78ms/step - loss: 0.0970 - accuracy: 0.9681 - val_loss: 0.9736 - val_accuracy: 0.8211 563/563 [==============================] - 7s 11ms/step Epoch 1/100 210/210 [==============================] - 24s 97ms/step - loss: 0.2569 - accuracy: 0.9347 - val_loss: 0.7809 - val_accuracy: 0.8416 Epoch 2/100 210/210 [==============================] - 22s 104ms/step - loss: 0.2149 - accuracy: 0.9390 - val_loss: 0.7297 - val_accuracy: 0.8353 Epoch 3/100 210/210 [==============================] - 22s 104ms/step - loss: 0.2210 - accuracy: 0.9410 - val_loss: 0.8037 - val_accuracy: 0.8384 Epoch 4/100 210/210 [==============================] - 23s 111ms/step - loss: 0.2224 - accuracy: 0.9398 - val_loss: 0.7140 - val_accuracy: 0.8374 Epoch 5/100 210/210 [==============================] - 23s 109ms/step - loss: 0.2058 - accuracy: 0.9407 - val_loss: 0.9194 - val_accuracy: 0.8024 Epoch 6/100 210/210 [==============================] - 23s 109ms/step - loss: 0.2015 - accuracy: 0.9414 - val_loss: 1.0289 - val_accuracy: 0.8031 Epoch 7/100 210/210 [==============================] - 24s 116ms/step - loss: 0.2100 - accuracy: 0.9415 - val_loss: 0.7459 - val_accuracy: 0.8144 Epoch 8/100 210/210 [==============================] - 26s 122ms/step - loss: 0.2047 - accuracy: 0.9421 - val_loss: 0.8850 - val_accuracy: 0.8372 Epoch 9/100 210/210 [==============================] - 26s 125ms/step - loss: 0.2022 - accuracy: 0.9428 - val_loss: 0.9464 - val_accuracy: 0.8070 Epoch 10/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1946 - accuracy: 0.9432 - val_loss: 0.8376 - val_accuracy: 0.8195 Epoch 11/100 210/210 [==============================] - 21s 102ms/step - loss: 0.2010 - accuracy: 0.9439 - val_loss: 0.7057 - val_accuracy: 0.8435 Epoch 12/100 210/210 [==============================] - 20s 96ms/step - loss: 0.1883 - accuracy: 0.9460 - val_loss: 0.9479 - val_accuracy: 0.8077 Epoch 13/100 210/210 [==============================] - 20s 97ms/step - loss: 0.1927 - accuracy: 0.9448 - val_loss: 0.9909 - val_accuracy: 0.7942 Epoch 14/100 210/210 [==============================] - 21s 102ms/step - loss: 0.1927 - accuracy: 0.9461 - val_loss: 0.8228 - val_accuracy: 0.8507 Epoch 15/100 210/210 [==============================] - 20s 97ms/step - loss: 0.1907 - accuracy: 0.9463 - val_loss: 0.9876 - val_accuracy: 0.8273 Epoch 16/100 210/210 [==============================] - 20s 96ms/step - loss: 0.1934 - accuracy: 0.9447 - val_loss: 1.1791 - val_accuracy: 0.8119 Epoch 17/100 210/210 [==============================] - 20s 97ms/step - loss: 0.1953 - accuracy: 0.9458 - val_loss: 0.8610 - val_accuracy: 0.8384 Epoch 18/100 210/210 [==============================] - 20s 96ms/step - loss: 0.1820 - accuracy: 0.9482 - val_loss: 0.9563 - val_accuracy: 0.8303 Epoch 19/100 210/210 [==============================] - 21s 100ms/step - loss: 0.1892 - accuracy: 0.9482 - val_loss: 0.8462 - val_accuracy: 0.8439 Epoch 20/100 210/210 [==============================] - 21s 98ms/step - loss: 0.1865 - accuracy: 0.9490 - val_loss: 0.8047 - val_accuracy: 0.8429 Epoch 21/100 210/210 [==============================] - 22s 103ms/step - loss: 0.1849 - accuracy: 0.9493 - val_loss: 0.8705 - val_accuracy: 0.7933 Epoch 22/100 210/210 [==============================] - 27s 127ms/step - loss: 0.1758 - accuracy: 0.9500 - val_loss: 1.0164 - val_accuracy: 0.8126 Epoch 23/100 210/210 [==============================] - 39s 186ms/step - loss: 0.1832 - accuracy: 0.9486 - val_loss: 0.8444 - val_accuracy: 0.8391 Epoch 24/100 210/210 [==============================] - 38s 181ms/step - loss: 0.1842 - accuracy: 0.9497 - val_loss: 0.8676 - val_accuracy: 0.8438 Epoch 25/100 210/210 [==============================] - 39s 184ms/step - loss: 0.1760 - accuracy: 0.9522 - val_loss: 1.4001 - val_accuracy: 0.7382 Epoch 26/100 210/210 [==============================] - 31s 147ms/step - loss: 0.1813 - accuracy: 0.9527 - val_loss: 0.9368 - val_accuracy: 0.8449 Epoch 27/100 210/210 [==============================] - 27s 127ms/step - loss: 0.1774 - accuracy: 0.9513 - val_loss: 1.1576 - val_accuracy: 0.7953 Epoch 28/100 210/210 [==============================] - 30s 145ms/step - loss: 0.1750 - accuracy: 0.9525 - val_loss: 1.0040 - val_accuracy: 0.8310 Epoch 29/100 210/210 [==============================] - 27s 131ms/step - loss: 0.1742 - accuracy: 0.9504 - val_loss: 0.9521 - val_accuracy: 0.8285 Epoch 30/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1648 - accuracy: 0.9541 - val_loss: 0.9570 - val_accuracy: 0.8479 Epoch 31/100 210/210 [==============================] - 36s 170ms/step - loss: 0.1715 - accuracy: 0.9534 - val_loss: 0.9095 - val_accuracy: 0.8219 Epoch 32/100 210/210 [==============================] - 31s 146ms/step - loss: 0.1679 - accuracy: 0.9528 - val_loss: 1.5571 - val_accuracy: 0.7847 Epoch 33/100 210/210 [==============================] - 30s 145ms/step - loss: 0.1677 - accuracy: 0.9527 - val_loss: 0.8631 - val_accuracy: 0.8379 Epoch 34/100 210/210 [==============================] - 31s 147ms/step - loss: 0.1785 - accuracy: 0.9525 - val_loss: 0.9753 - val_accuracy: 0.8431 Epoch 35/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1709 - accuracy: 0.9517 - val_loss: 0.9560 - val_accuracy: 0.8164 Epoch 36/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1756 - accuracy: 0.9520 - val_loss: 1.1301 - val_accuracy: 0.7889 Epoch 37/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1706 - accuracy: 0.9524 - val_loss: 1.0202 - val_accuracy: 0.7837 Epoch 38/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1551 - accuracy: 0.9568 - val_loss: 1.0160 - val_accuracy: 0.8414 Epoch 39/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1644 - accuracy: 0.9535 - val_loss: 1.0991 - val_accuracy: 0.8332 Epoch 40/100 210/210 [==============================] - 29s 136ms/step - loss: 0.1599 - accuracy: 0.9548 - val_loss: 0.8419 - val_accuracy: 0.8469 Epoch 41/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1605 - accuracy: 0.9568 - val_loss: 0.9097 - val_accuracy: 0.8391 Epoch 42/100 210/210 [==============================] - 30s 144ms/step - loss: 0.1692 - accuracy: 0.9542 - val_loss: 0.8577 - val_accuracy: 0.8507 Epoch 43/100 210/210 [==============================] - 30s 143ms/step - loss: 0.1598 - accuracy: 0.9554 - val_loss: 1.0090 - val_accuracy: 0.8368 Epoch 44/100 210/210 [==============================] - 30s 142ms/step - loss: 0.1643 - accuracy: 0.9553 - val_loss: 0.8956 - val_accuracy: 0.8234 Epoch 45/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1546 - accuracy: 0.9582 - val_loss: 1.3838 - val_accuracy: 0.8063 Epoch 46/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1742 - accuracy: 0.9534 - val_loss: 0.8086 - val_accuracy: 0.8391 Epoch 47/100 210/210 [==============================] - 28s 136ms/step - loss: 0.1639 - accuracy: 0.9561 - val_loss: 0.8921 - val_accuracy: 0.8258 Epoch 48/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 1.1120 - val_accuracy: 0.8321 Epoch 49/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1536 - accuracy: 0.9579 - val_loss: 1.1588 - val_accuracy: 0.8451 Epoch 50/100 210/210 [==============================] - 30s 142ms/step - loss: 0.1606 - accuracy: 0.9565 - val_loss: 0.9612 - val_accuracy: 0.8336 Epoch 51/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1509 - accuracy: 0.9587 - val_loss: 1.0633 - val_accuracy: 0.8382 Epoch 52/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1602 - accuracy: 0.9581 - val_loss: 1.0785 - val_accuracy: 0.8369 Epoch 53/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1563 - accuracy: 0.9597 - val_loss: 1.0667 - val_accuracy: 0.8420 Epoch 54/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1586 - accuracy: 0.9575 - val_loss: 1.0411 - val_accuracy: 0.8344 Epoch 55/100 210/210 [==============================] - 30s 141ms/step - loss: 0.1576 - accuracy: 0.9573 - val_loss: 0.9543 - val_accuracy: 0.8342 Epoch 56/100 210/210 [==============================] - 30s 141ms/step - loss: 0.1450 - accuracy: 0.9590 - val_loss: 0.9851 - val_accuracy: 0.8199 Epoch 57/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1554 - accuracy: 0.9586 - val_loss: 1.0457 - val_accuracy: 0.8402 Epoch 58/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1539 - accuracy: 0.9566 - val_loss: 0.9191 - val_accuracy: 0.8114 Epoch 59/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1605 - accuracy: 0.9579 - val_loss: 0.9416 - val_accuracy: 0.8407 Epoch 60/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1576 - accuracy: 0.9593 - val_loss: 1.0324 - val_accuracy: 0.8364 Epoch 61/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1570 - accuracy: 0.9569 - val_loss: 1.3240 - val_accuracy: 0.8253 Epoch 62/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1664 - accuracy: 0.9543 - val_loss: 0.8582 - val_accuracy: 0.8444 Epoch 63/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1596 - accuracy: 0.9580 - val_loss: 1.1260 - val_accuracy: 0.7723 Epoch 64/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1483 - accuracy: 0.9594 - val_loss: 0.9080 - val_accuracy: 0.8423 Epoch 65/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1555 - accuracy: 0.9595 - val_loss: 1.1142 - val_accuracy: 0.8276 Epoch 66/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1571 - accuracy: 0.9587 - val_loss: 0.8549 - val_accuracy: 0.8392 Epoch 67/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1528 - accuracy: 0.9590 - val_loss: 1.3959 - val_accuracy: 0.7806 Epoch 68/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1514 - accuracy: 0.9590 - val_loss: 1.1074 - val_accuracy: 0.8050 Epoch 69/100 210/210 [==============================] - 28s 134ms/step - loss: 0.1536 - accuracy: 0.9604 - val_loss: 1.2713 - val_accuracy: 0.7907 Epoch 70/100 210/210 [==============================] - 28s 134ms/step - loss: 0.1534 - accuracy: 0.9584 - val_loss: 1.1036 - val_accuracy: 0.8386 Epoch 71/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1587 - accuracy: 0.9579 - val_loss: 1.1526 - val_accuracy: 0.8179 Epoch 72/100 210/210 [==============================] - 29s 136ms/step - loss: 0.1614 - accuracy: 0.9582 - val_loss: 1.0746 - val_accuracy: 0.8390 Epoch 73/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1555 - accuracy: 0.9589 - val_loss: 1.0895 - val_accuracy: 0.8290 Epoch 74/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1529 - accuracy: 0.9600 - val_loss: 0.9483 - val_accuracy: 0.8417 Epoch 75/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1399 - accuracy: 0.9628 - val_loss: 1.0456 - val_accuracy: 0.7996 Epoch 76/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1559 - accuracy: 0.9598 - val_loss: 1.0342 - val_accuracy: 0.8330 Epoch 77/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1476 - accuracy: 0.9620 - val_loss: 1.0522 - val_accuracy: 0.8321 Epoch 78/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1486 - accuracy: 0.9606 - val_loss: 1.0882 - val_accuracy: 0.8469 Epoch 79/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1487 - accuracy: 0.9611 - val_loss: 1.0531 - val_accuracy: 0.8115 Epoch 80/100 210/210 [==============================] - 28s 134ms/step - loss: 0.1557 - accuracy: 0.9601 - val_loss: 1.1172 - val_accuracy: 0.8222 Epoch 81/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1539 - accuracy: 0.9610 - val_loss: 1.0784 - val_accuracy: 0.8359 Epoch 82/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1517 - accuracy: 0.9602 - val_loss: 1.2762 - val_accuracy: 0.8047 Epoch 83/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1432 - accuracy: 0.9624 - val_loss: 1.0279 - val_accuracy: 0.8403 Epoch 84/100 210/210 [==============================] - 29s 136ms/step - loss: 0.1528 - accuracy: 0.9622 - val_loss: 1.0766 - val_accuracy: 0.8207 Epoch 85/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1479 - accuracy: 0.9609 - val_loss: 1.2798 - val_accuracy: 0.7980 Epoch 86/100 210/210 [==============================] - 28s 136ms/step - loss: 0.1505 - accuracy: 0.9604 - val_loss: 1.1814 - val_accuracy: 0.8278 Epoch 87/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1567 - accuracy: 0.9610 - val_loss: 1.1422 - val_accuracy: 0.8447 Epoch 88/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1472 - accuracy: 0.9614 - val_loss: 1.4859 - val_accuracy: 0.7869 Epoch 89/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1498 - accuracy: 0.9613 - val_loss: 1.1179 - val_accuracy: 0.8425 Epoch 90/100 210/210 [==============================] - 28s 136ms/step - loss: 0.1533 - accuracy: 0.9598 - val_loss: 1.3730 - val_accuracy: 0.8016 Epoch 91/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1459 - accuracy: 0.9610 - val_loss: 1.3234 - val_accuracy: 0.8149 Epoch 92/100 210/210 [==============================] - 28s 136ms/step - loss: 0.1543 - accuracy: 0.9599 - val_loss: 0.9662 - val_accuracy: 0.8356 Epoch 93/100 210/210 [==============================] - 28s 135ms/step - loss: 0.1528 - accuracy: 0.9614 - val_loss: 1.2638 - val_accuracy: 0.8358 Epoch 94/100 210/210 [==============================] - 28s 136ms/step - loss: 0.1559 - accuracy: 0.9586 - val_loss: 1.3600 - val_accuracy: 0.8058 Epoch 95/100 210/210 [==============================] - 29s 140ms/step - loss: 0.1507 - accuracy: 0.9610 - val_loss: 1.2974 - val_accuracy: 0.8330 Epoch 96/100 210/210 [==============================] - 29s 139ms/step - loss: 0.1554 - accuracy: 0.9605 - val_loss: 1.4003 - val_accuracy: 0.7828 Epoch 97/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1541 - accuracy: 0.9598 - val_loss: 1.1915 - val_accuracy: 0.8148 Epoch 98/100 210/210 [==============================] - 29s 136ms/step - loss: 0.1432 - accuracy: 0.9631 - val_loss: 1.0654 - val_accuracy: 0.8393 Epoch 99/100 210/210 [==============================] - 29s 136ms/step - loss: 0.1532 - accuracy: 0.9603 - val_loss: 1.0889 - val_accuracy: 0.8336 Epoch 100/100 210/210 [==============================] - 29s 137ms/step - loss: 0.1446 - accuracy: 0.9633 - val_loss: 1.3120 - val_accuracy: 0.8100 563/563 [==============================] - 11s 19ms/step Epoch 1/100 210/210 [==============================] - 27s 120ms/step - loss: 0.1896 - accuracy: 0.9474 - val_loss: 1.2258 - val_accuracy: 0.8207 Epoch 2/100 210/210 [==============================] - 24s 116ms/step - loss: 0.1374 - accuracy: 0.9609 - val_loss: 1.1758 - val_accuracy: 0.8269 Epoch 3/100 210/210 [==============================] - 24s 115ms/step - loss: 0.1036 - accuracy: 0.9701 - val_loss: 1.1464 - val_accuracy: 0.8319 Epoch 4/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0816 - accuracy: 0.9760 - val_loss: 1.1255 - val_accuracy: 0.8358 Epoch 5/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0665 - accuracy: 0.9803 - val_loss: 1.1116 - val_accuracy: 0.8396 Epoch 6/100 210/210 [==============================] - 25s 117ms/step - loss: 0.0547 - accuracy: 0.9844 - val_loss: 1.1011 - val_accuracy: 0.8431 Epoch 7/100 210/210 [==============================] - 24s 117ms/step - loss: 0.0462 - accuracy: 0.9870 - val_loss: 1.0952 - val_accuracy: 0.8452 Epoch 8/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0403 - accuracy: 0.9890 - val_loss: 1.0926 - val_accuracy: 0.8471 Epoch 9/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0358 - accuracy: 0.9903 - val_loss: 1.0918 - val_accuracy: 0.8481 Epoch 10/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0326 - accuracy: 0.9914 - val_loss: 1.0932 - val_accuracy: 0.8492 Epoch 11/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0299 - accuracy: 0.9923 - val_loss: 1.0961 - val_accuracy: 0.8499 Epoch 12/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0280 - accuracy: 0.9926 - val_loss: 1.1006 - val_accuracy: 0.8505 Epoch 13/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0265 - accuracy: 0.9931 - val_loss: 1.1058 - val_accuracy: 0.8503 Epoch 14/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0252 - accuracy: 0.9933 - val_loss: 1.1115 - val_accuracy: 0.8510 Epoch 15/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0242 - accuracy: 0.9938 - val_loss: 1.1177 - val_accuracy: 0.8515 Epoch 16/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0234 - accuracy: 0.9940 - val_loss: 1.1239 - val_accuracy: 0.8516 Epoch 17/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0226 - accuracy: 0.9941 - val_loss: 1.1304 - val_accuracy: 0.8518 Epoch 18/100 210/210 [==============================] - 24s 117ms/step - loss: 0.0219 - accuracy: 0.9943 - val_loss: 1.1368 - val_accuracy: 0.8514 Epoch 19/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0213 - accuracy: 0.9945 - val_loss: 1.1434 - val_accuracy: 0.8516 Epoch 20/100 210/210 [==============================] - 25s 119ms/step - loss: 0.0207 - accuracy: 0.9947 - val_loss: 1.1498 - val_accuracy: 0.8516 Epoch 21/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0201 - accuracy: 0.9948 - val_loss: 1.1561 - val_accuracy: 0.8514 Epoch 22/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0196 - accuracy: 0.9950 - val_loss: 1.1623 - val_accuracy: 0.8517 Epoch 23/100 210/210 [==============================] - 25s 119ms/step - loss: 0.0192 - accuracy: 0.9950 - val_loss: 1.1687 - val_accuracy: 0.8519 Epoch 24/100 210/210 [==============================] - 25s 117ms/step - loss: 0.0187 - accuracy: 0.9950 - val_loss: 1.1752 - val_accuracy: 0.8521 Epoch 25/100 210/210 [==============================] - 25s 120ms/step - loss: 0.0183 - accuracy: 0.9951 - val_loss: 1.1818 - val_accuracy: 0.8522 Epoch 26/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0179 - accuracy: 0.9954 - val_loss: 1.1883 - val_accuracy: 0.8522 Epoch 27/100 210/210 [==============================] - 25s 118ms/step - loss: 0.0176 - accuracy: 0.9956 - val_loss: 1.1947 - val_accuracy: 0.8523 Epoch 28/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0172 - accuracy: 0.9957 - val_loss: 1.2012 - val_accuracy: 0.8524 Epoch 29/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0169 - accuracy: 0.9957 - val_loss: 1.2075 - val_accuracy: 0.8526 Epoch 30/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0166 - accuracy: 0.9957 - val_loss: 1.2139 - val_accuracy: 0.8529 Epoch 31/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0163 - accuracy: 0.9958 - val_loss: 1.2202 - val_accuracy: 0.8528 Epoch 32/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0161 - accuracy: 0.9959 - val_loss: 1.2264 - val_accuracy: 0.8529 Epoch 33/100 210/210 [==============================] - 24s 114ms/step - loss: 0.0158 - accuracy: 0.9959 - val_loss: 1.2324 - val_accuracy: 0.8529 Epoch 34/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0156 - accuracy: 0.9960 - val_loss: 1.2383 - val_accuracy: 0.8528 Epoch 35/100 210/210 [==============================] - 24s 116ms/step - loss: 0.0153 - accuracy: 0.9961 - val_loss: 1.2442 - val_accuracy: 0.8528 Epoch 36/100 210/210 [==============================] - 24s 114ms/step - loss: 0.0151 - accuracy: 0.9961 - val_loss: 1.2500 - val_accuracy: 0.8528 Epoch 37/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0149 - accuracy: 0.9961 - val_loss: 1.2558 - val_accuracy: 0.8528 Epoch 38/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0147 - accuracy: 0.9961 - val_loss: 1.2617 - val_accuracy: 0.8529 Epoch 39/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0145 - accuracy: 0.9962 - val_loss: 1.2674 - val_accuracy: 0.8529 Epoch 40/100 210/210 [==============================] - 24s 115ms/step - loss: 0.0143 - accuracy: 0.9962 - val_loss: 1.2731 - val_accuracy: 0.8528 Epoch 41/100 210/210 [==============================] - 33s 158ms/step - loss: 0.0141 - accuracy: 0.9963 - val_loss: 1.2785 - val_accuracy: 0.8530 Epoch 42/100 210/210 [==============================] - 29s 136ms/step - loss: 0.0139 - accuracy: 0.9963 - val_loss: 1.2841 - val_accuracy: 0.8532 Epoch 43/100 210/210 [==============================] - 28s 134ms/step - loss: 0.0137 - accuracy: 0.9963 - val_loss: 1.2896 - val_accuracy: 0.8532 Epoch 44/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 1.2950 - val_accuracy: 0.8531 Epoch 45/100 210/210 [==============================] - 29s 136ms/step - loss: 0.0134 - accuracy: 0.9964 - val_loss: 1.3001 - val_accuracy: 0.8531 Epoch 46/100 210/210 [==============================] - 29s 138ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 1.3052 - val_accuracy: 0.8533 Epoch 47/100 210/210 [==============================] - 28s 134ms/step - loss: 0.0130 - accuracy: 0.9965 - val_loss: 1.3104 - val_accuracy: 0.8533 Epoch 48/100 210/210 [==============================] - 28s 134ms/step - loss: 0.0129 - accuracy: 0.9965 - val_loss: 1.3153 - val_accuracy: 0.8536 Epoch 49/100 210/210 [==============================] - 28s 134ms/step - loss: 0.0127 - accuracy: 0.9965 - val_loss: 1.3207 - val_accuracy: 0.8535 Epoch 50/100 210/210 [==============================] - 29s 138ms/step - loss: 0.0126 - accuracy: 0.9966 - val_loss: 1.3259 - val_accuracy: 0.8536 Epoch 51/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0124 - accuracy: 0.9966 - val_loss: 1.3309 - val_accuracy: 0.8535 Epoch 52/100 210/210 [==============================] - 29s 136ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 1.3360 - val_accuracy: 0.8534 Epoch 53/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0121 - accuracy: 0.9966 - val_loss: 1.3410 - val_accuracy: 0.8536 Epoch 54/100 210/210 [==============================] - 31s 146ms/step - loss: 0.0120 - accuracy: 0.9966 - val_loss: 1.3461 - val_accuracy: 0.8537 Epoch 55/100 210/210 [==============================] - 29s 140ms/step - loss: 0.0119 - accuracy: 0.9967 - val_loss: 1.3508 - val_accuracy: 0.8537 Epoch 56/100 210/210 [==============================] - 29s 139ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 1.3556 - val_accuracy: 0.8537 Epoch 57/100 210/210 [==============================] - 30s 140ms/step - loss: 0.0116 - accuracy: 0.9969 - val_loss: 1.3604 - val_accuracy: 0.8536 Epoch 58/100 210/210 [==============================] - 29s 140ms/step - loss: 0.0115 - accuracy: 0.9969 - val_loss: 1.3652 - val_accuracy: 0.8537 Epoch 59/100 210/210 [==============================] - 30s 141ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 1.3699 - val_accuracy: 0.8539 Epoch 60/100 210/210 [==============================] - 30s 145ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 1.3746 - val_accuracy: 0.8538 Epoch 61/100 210/210 [==============================] - 30639s 147s/step - loss: 0.0111 - accuracy: 0.9969 - val_loss: 1.3790 - val_accuracy: 0.8537 Epoch 62/100 210/210 [==============================] - 31s 146ms/step - loss: 0.0110 - accuracy: 0.9969 - val_loss: 1.3837 - val_accuracy: 0.8536 Epoch 63/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0109 - accuracy: 0.9969 - val_loss: 1.3883 - val_accuracy: 0.8537 Epoch 64/100 210/210 [==============================] - 31s 147ms/step - loss: 0.0108 - accuracy: 0.9969 - val_loss: 1.3928 - val_accuracy: 0.8537 Epoch 65/100 210/210 [==============================] - 30s 145ms/step - loss: 0.0107 - accuracy: 0.9970 - val_loss: 1.3973 - val_accuracy: 0.8539 Epoch 66/100 210/210 [==============================] - 29s 140ms/step - loss: 0.0106 - accuracy: 0.9970 - val_loss: 1.4018 - val_accuracy: 0.8539 Epoch 67/100 210/210 [==============================] - 22s 104ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 1.4063 - val_accuracy: 0.8539 Epoch 68/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0104 - accuracy: 0.9970 - val_loss: 1.4108 - val_accuracy: 0.8539 Epoch 69/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0103 - accuracy: 0.9971 - val_loss: 1.4151 - val_accuracy: 0.8539 Epoch 70/100 210/210 [==============================] - 22s 106ms/step - loss: 0.0103 - accuracy: 0.9971 - val_loss: 1.4195 - val_accuracy: 0.8538 Epoch 71/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0102 - accuracy: 0.9972 - val_loss: 1.4240 - val_accuracy: 0.8539 Epoch 72/100 210/210 [==============================] - 22s 102ms/step - loss: 0.0101 - accuracy: 0.9972 - val_loss: 1.4284 - val_accuracy: 0.8541 Epoch 73/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 1.4328 - val_accuracy: 0.8543 Epoch 74/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0099 - accuracy: 0.9972 - val_loss: 1.4372 - val_accuracy: 0.8543 Epoch 75/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0098 - accuracy: 0.9973 - val_loss: 1.4414 - val_accuracy: 0.8543 Epoch 76/100 210/210 [==============================] - 22s 105ms/step - loss: 0.0097 - accuracy: 0.9973 - val_loss: 1.4457 - val_accuracy: 0.8543 Epoch 77/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0097 - accuracy: 0.9973 - val_loss: 1.4500 - val_accuracy: 0.8541 Epoch 78/100 210/210 [==============================] - 22s 103ms/step - loss: 0.0096 - accuracy: 0.9974 - val_loss: 1.4541 - val_accuracy: 0.8541 Epoch 79/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0095 - accuracy: 0.9973 - val_loss: 1.4582 - val_accuracy: 0.8541 Epoch 80/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 1.4623 - val_accuracy: 0.8542 Epoch 81/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 1.4662 - val_accuracy: 0.8541 Epoch 82/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0093 - accuracy: 0.9974 - val_loss: 1.4701 - val_accuracy: 0.8542 Epoch 83/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0092 - accuracy: 0.9974 - val_loss: 1.4743 - val_accuracy: 0.8541 Epoch 84/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0091 - accuracy: 0.9975 - val_loss: 1.4784 - val_accuracy: 0.8540 Epoch 85/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0091 - accuracy: 0.9975 - val_loss: 1.4824 - val_accuracy: 0.8542 Epoch 86/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0090 - accuracy: 0.9975 - val_loss: 1.4865 - val_accuracy: 0.8539 Epoch 87/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 1.4905 - val_accuracy: 0.8539 Epoch 88/100 210/210 [==============================] - 22s 103ms/step - loss: 0.0088 - accuracy: 0.9975 - val_loss: 1.4945 - val_accuracy: 0.8540 Epoch 89/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0088 - accuracy: 0.9975 - val_loss: 1.4985 - val_accuracy: 0.8540 Epoch 90/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0087 - accuracy: 0.9975 - val_loss: 1.5026 - val_accuracy: 0.8541 Epoch 91/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0086 - accuracy: 0.9975 - val_loss: 1.5067 - val_accuracy: 0.8541 Epoch 92/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0086 - accuracy: 0.9975 - val_loss: 1.5105 - val_accuracy: 0.8541 Epoch 93/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0085 - accuracy: 0.9975 - val_loss: 1.5144 - val_accuracy: 0.8541 Epoch 94/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0085 - accuracy: 0.9976 - val_loss: 1.5185 - val_accuracy: 0.8541 Epoch 95/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0084 - accuracy: 0.9976 - val_loss: 1.5225 - val_accuracy: 0.8542 Epoch 96/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 1.5265 - val_accuracy: 0.8542 Epoch 97/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 1.5303 - val_accuracy: 0.8541 Epoch 98/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 1.5343 - val_accuracy: 0.8541 Epoch 99/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0082 - accuracy: 0.9977 - val_loss: 1.5382 - val_accuracy: 0.8540 Epoch 100/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 1.5421 - val_accuracy: 0.8540 563/563 [==============================] - 11s 18ms/step Epoch 1/100 210/210 [==============================] - 21s 91ms/step - loss: 0.0082 - accuracy: 0.9977 - val_loss: 1.5901 - val_accuracy: 0.8542 Epoch 2/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0069 - accuracy: 0.9982 - val_loss: 1.6407 - val_accuracy: 0.8548 Epoch 3/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0063 - accuracy: 0.9983 - val_loss: 1.6796 - val_accuracy: 0.8550 Epoch 4/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 1.7166 - val_accuracy: 0.8548 Epoch 5/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0054 - accuracy: 0.9986 - val_loss: 1.7505 - val_accuracy: 0.8546 Epoch 6/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 1.7873 - val_accuracy: 0.8546 Epoch 7/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 1.8212 - val_accuracy: 0.8544 Epoch 8/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 1.8491 - val_accuracy: 0.8542 Epoch 9/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 1.8813 - val_accuracy: 0.8541 Epoch 10/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 1.9058 - val_accuracy: 0.8537 Epoch 11/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 1.9339 - val_accuracy: 0.8540 Epoch 12/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0039 - accuracy: 0.9989 - val_loss: 1.9531 - val_accuracy: 0.8546 Epoch 13/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 1.9770 - val_accuracy: 0.8546 Epoch 14/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 2.0003 - val_accuracy: 0.8543 Epoch 15/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 2.0192 - val_accuracy: 0.8545 Epoch 16/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 2.0377 - val_accuracy: 0.8542 Epoch 17/100 210/210 [==============================] - 22s 106ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 2.0572 - val_accuracy: 0.8547 Epoch 18/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 2.0712 - val_accuracy: 0.8544 Epoch 19/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 2.0888 - val_accuracy: 0.8544 Epoch 20/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 2.1059 - val_accuracy: 0.8541 Epoch 21/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 2.1224 - val_accuracy: 0.8544 Epoch 22/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 2.1370 - val_accuracy: 0.8541 Epoch 23/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 2.1534 - val_accuracy: 0.8544 Epoch 24/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 2.1691 - val_accuracy: 0.8537 Epoch 25/100 210/210 [==============================] - 23s 108ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 2.1850 - val_accuracy: 0.8544 Epoch 26/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 2.1983 - val_accuracy: 0.8543 Epoch 27/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 2.2089 - val_accuracy: 0.8544 Epoch 28/100 210/210 [==============================] - 22s 103ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 2.2333 - val_accuracy: 0.8540 Epoch 29/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 2.2422 - val_accuracy: 0.8541 Epoch 30/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 2.2539 - val_accuracy: 0.8542 Epoch 31/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 2.2677 - val_accuracy: 0.8540 Epoch 32/100 210/210 [==============================] - 19s 93ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 2.2793 - val_accuracy: 0.8539 Epoch 33/100 210/210 [==============================] - 19s 93ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 2.2934 - val_accuracy: 0.8541 Epoch 34/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 2.3063 - val_accuracy: 0.8540 Epoch 35/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 2.3184 - val_accuracy: 0.8541 Epoch 36/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.3286 - val_accuracy: 0.8541 Epoch 37/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.3400 - val_accuracy: 0.8538 Epoch 38/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.3491 - val_accuracy: 0.8542 Epoch 39/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.3586 - val_accuracy: 0.8540 Epoch 40/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.3701 - val_accuracy: 0.8543 Epoch 41/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.3799 - val_accuracy: 0.8538 Epoch 42/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 2.3899 - val_accuracy: 0.8543 Epoch 43/100 210/210 [==============================] - 18s 85ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 2.4027 - val_accuracy: 0.8543 Epoch 44/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 2.4116 - val_accuracy: 0.8542 Epoch 45/100 210/210 [==============================] - 18s 88ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.4198 - val_accuracy: 0.8545 Epoch 46/100 210/210 [==============================] - 18s 84ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.4328 - val_accuracy: 0.8543 Epoch 47/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 2.4409 - val_accuracy: 0.8542 Epoch 48/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.4516 - val_accuracy: 0.8542 Epoch 49/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.4594 - val_accuracy: 0.8544 Epoch 50/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 2.4697 - val_accuracy: 0.8539 Epoch 51/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 2.4803 - val_accuracy: 0.8543 Epoch 52/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 2.4889 - val_accuracy: 0.8544 Epoch 53/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 2.4971 - val_accuracy: 0.8543 Epoch 54/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 2.5056 - val_accuracy: 0.8543 Epoch 55/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 2.5150 - val_accuracy: 0.8546 Epoch 56/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 2.5239 - val_accuracy: 0.8546 Epoch 57/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 2.5325 - val_accuracy: 0.8546 Epoch 58/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 2.5406 - val_accuracy: 0.8548 Epoch 59/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 2.5486 - val_accuracy: 0.8547 Epoch 60/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 2.5568 - val_accuracy: 0.8549 Epoch 61/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.5660 - val_accuracy: 0.8550 Epoch 62/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.5747 - val_accuracy: 0.8551 Epoch 63/100 210/210 [==============================] - 18s 88ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.5826 - val_accuracy: 0.8552 Epoch 64/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.5918 - val_accuracy: 0.8550 Epoch 65/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.6000 - val_accuracy: 0.8548 Epoch 66/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.6099 - val_accuracy: 0.8549 Epoch 67/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.6176 - val_accuracy: 0.8552 Epoch 68/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.6264 - val_accuracy: 0.8553 Epoch 69/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.6343 - val_accuracy: 0.8551 Epoch 70/100 210/210 [==============================] - 18s 88ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 2.6408 - val_accuracy: 0.8553 Epoch 71/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 2.6489 - val_accuracy: 0.8551 Epoch 72/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 2.6570 - val_accuracy: 0.8551 Epoch 73/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.6649 - val_accuracy: 0.8552 Epoch 74/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.6728 - val_accuracy: 0.8552 Epoch 75/100 210/210 [==============================] - 21s 100ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.6802 - val_accuracy: 0.8554 Epoch 76/100 210/210 [==============================] - 19s 93ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.6889 - val_accuracy: 0.8551 Epoch 77/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.6953 - val_accuracy: 0.8554 Epoch 78/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.7024 - val_accuracy: 0.8551 Epoch 79/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.7099 - val_accuracy: 0.8552 Epoch 80/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.7171 - val_accuracy: 0.8552 Epoch 81/100 210/210 [==============================] - 18s 88ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.7251 - val_accuracy: 0.8552 Epoch 82/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.7311 - val_accuracy: 0.8551 Epoch 83/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.7385 - val_accuracy: 0.8552 Epoch 84/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.7462 - val_accuracy: 0.8551 Epoch 85/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7529 - val_accuracy: 0.8552 Epoch 86/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7585 - val_accuracy: 0.8553 Epoch 87/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7644 - val_accuracy: 0.8549 Epoch 88/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7723 - val_accuracy: 0.8547 Epoch 89/100 210/210 [==============================] - 18s 87ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7788 - val_accuracy: 0.8550 Epoch 90/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7848 - val_accuracy: 0.8548 Epoch 91/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7903 - val_accuracy: 0.8550 Epoch 92/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.7987 - val_accuracy: 0.8551 Epoch 93/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.8027 - val_accuracy: 0.8551 Epoch 94/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.8103 - val_accuracy: 0.8548 Epoch 95/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8165 - val_accuracy: 0.8551 Epoch 96/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8238 - val_accuracy: 0.8549 Epoch 97/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8279 - val_accuracy: 0.8547 Epoch 98/100 210/210 [==============================] - 18s 86ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8342 - val_accuracy: 0.8547 Epoch 99/100 210/210 [==============================] - 19s 88ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8403 - val_accuracy: 0.8546 Epoch 100/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 2.8471 - val_accuracy: 0.8553 563/563 [==============================] - 8s 14ms/step Epoch 1/100 210/210 [==============================] - 22s 95ms/step - loss: 0.0235 - accuracy: 0.9945 - val_loss: 2.0966 - val_accuracy: 0.8516 Epoch 2/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0058 - accuracy: 0.9981 - val_loss: 2.2330 - val_accuracy: 0.8538 Epoch 3/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0070 - accuracy: 0.9979 - val_loss: 2.1520 - val_accuracy: 0.8531 Epoch 4/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 2.2925 - val_accuracy: 0.8546 Epoch 5/100 210/210 [==============================] - 22s 103ms/step - loss: 0.0064 - accuracy: 0.9980 - val_loss: 2.3969 - val_accuracy: 0.8486 Epoch 6/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0074 - accuracy: 0.9978 - val_loss: 2.3003 - val_accuracy: 0.8526 Epoch 7/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 2.2984 - val_accuracy: 0.8481 Epoch 8/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 2.3384 - val_accuracy: 0.8527 Epoch 9/100 210/210 [==============================] - 19s 89ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 2.3241 - val_accuracy: 0.8539 Epoch 10/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0055 - accuracy: 0.9985 - val_loss: 2.3985 - val_accuracy: 0.8541 Epoch 11/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 2.4449 - val_accuracy: 0.8536 Epoch 12/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 2.4357 - val_accuracy: 0.8504 Epoch 13/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 2.4969 - val_accuracy: 0.8518 Epoch 14/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0058 - accuracy: 0.9984 - val_loss: 2.3386 - val_accuracy: 0.8543 Epoch 15/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 2.6053 - val_accuracy: 0.8529 Epoch 16/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0074 - accuracy: 0.9982 - val_loss: 2.2973 - val_accuracy: 0.8518 Epoch 17/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 2.4302 - val_accuracy: 0.8537 Epoch 18/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 2.6306 - val_accuracy: 0.8501 Epoch 19/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 2.6296 - val_accuracy: 0.8528 Epoch 20/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.7480 - val_accuracy: 0.8536 Epoch 21/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 2.7945 - val_accuracy: 0.8513 Epoch 22/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0056 - accuracy: 0.9980 - val_loss: 2.6582 - val_accuracy: 0.8532 Epoch 23/100 210/210 [==============================] - 21s 100ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 2.6530 - val_accuracy: 0.8527 Epoch 24/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 2.8370 - val_accuracy: 0.8529 Epoch 25/100 210/210 [==============================] - 21s 101ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.9347 - val_accuracy: 0.8524 Epoch 26/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 3.1013 - val_accuracy: 0.8488 Epoch 27/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 2.8676 - val_accuracy: 0.8514 Epoch 28/100 210/210 [==============================] - 22s 103ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 2.7683 - val_accuracy: 0.8495 Epoch 29/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 2.9357 - val_accuracy: 0.8518 Epoch 30/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 2.6892 - val_accuracy: 0.8524 Epoch 31/100 210/210 [==============================] - 21s 100ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 2.8643 - val_accuracy: 0.8539 Epoch 32/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0022 - accuracy: 0.9991 - val_loss: 3.0200 - val_accuracy: 0.8545 Epoch 33/100 210/210 [==============================] - 20s 93ms/step - loss: 7.9494e-04 - accuracy: 0.9998 - val_loss: 3.1127 - val_accuracy: 0.8550 Epoch 34/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 3.0521 - val_accuracy: 0.8473 Epoch 35/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 2.9422 - val_accuracy: 0.8536 Epoch 36/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 3.0572 - val_accuracy: 0.8533 Epoch 37/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 3.0919 - val_accuracy: 0.8559 Epoch 38/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 2.9134 - val_accuracy: 0.8514 Epoch 39/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 3.0292 - val_accuracy: 0.8563 Epoch 40/100 210/210 [==============================] - 19s 92ms/step - loss: 4.6114e-04 - accuracy: 0.9999 - val_loss: 3.1599 - val_accuracy: 0.8540 Epoch 41/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 2.9956 - val_accuracy: 0.8539 Epoch 42/100 210/210 [==============================] - 20s 94ms/step - loss: 9.1427e-04 - accuracy: 0.9998 - val_loss: 3.2080 - val_accuracy: 0.8537 Epoch 43/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 3.1307 - val_accuracy: 0.8509 Epoch 44/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 3.2098 - val_accuracy: 0.8526 Epoch 45/100 210/210 [==============================] - 20s 98ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 3.2745 - val_accuracy: 0.8519 Epoch 46/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 3.5174 - val_accuracy: 0.8488 Epoch 47/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 2.8968 - val_accuracy: 0.8523 Epoch 48/100 210/210 [==============================] - 19s 89ms/step - loss: 5.3029e-04 - accuracy: 0.9999 - val_loss: 3.0893 - val_accuracy: 0.8528 Epoch 49/100 210/210 [==============================] - 19s 91ms/step - loss: 3.4504e-04 - accuracy: 0.9999 - val_loss: 3.2071 - val_accuracy: 0.8536 Epoch 50/100 210/210 [==============================] - 19s 92ms/step - loss: 4.7167e-04 - accuracy: 0.9998 - val_loss: 3.3061 - val_accuracy: 0.8521 Epoch 51/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0055 - accuracy: 0.9985 - val_loss: 3.1269 - val_accuracy: 0.8521 Epoch 52/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 3.1754 - val_accuracy: 0.8547 Epoch 53/100 210/210 [==============================] - 19s 92ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 3.3030 - val_accuracy: 0.8506 Epoch 54/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 3.3517 - val_accuracy: 0.8533 Epoch 55/100 210/210 [==============================] - 20s 97ms/step - loss: 4.5901e-04 - accuracy: 0.9998 - val_loss: 3.4710 - val_accuracy: 0.8539 Epoch 56/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 3.4185 - val_accuracy: 0.8469 Epoch 57/100 210/210 [==============================] - 21s 100ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 3.0337 - val_accuracy: 0.8534 Epoch 58/100 210/210 [==============================] - 19s 91ms/step - loss: 3.3640e-04 - accuracy: 0.9999 - val_loss: 3.1835 - val_accuracy: 0.8521 Epoch 59/100 210/210 [==============================] - 19s 91ms/step - loss: 2.8025e-04 - accuracy: 0.9999 - val_loss: 3.2979 - val_accuracy: 0.8529 Epoch 60/100 210/210 [==============================] - 21s 98ms/step - loss: 1.9957e-04 - accuracy: 1.0000 - val_loss: 3.3682 - val_accuracy: 0.8535 Epoch 61/100 210/210 [==============================] - 19s 90ms/step - loss: 1.7355e-04 - accuracy: 1.0000 - val_loss: 3.5218 - val_accuracy: 0.8531 Epoch 62/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 3.3558 - val_accuracy: 0.8508 Epoch 63/100 210/210 [==============================] - 20s 97ms/step - loss: 5.1059e-04 - accuracy: 0.9999 - val_loss: 3.4354 - val_accuracy: 0.8542 Epoch 64/100 210/210 [==============================] - 19s 91ms/step - loss: 5.4886e-04 - accuracy: 0.9998 - val_loss: 3.5908 - val_accuracy: 0.8522 Epoch 65/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 3.4685 - val_accuracy: 0.8525 Epoch 66/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 3.4525 - val_accuracy: 0.8504 Epoch 67/100 210/210 [==============================] - 19s 90ms/step - loss: 0.0025 - accuracy: 0.9990 - val_loss: 3.4114 - val_accuracy: 0.8516 Epoch 68/100 210/210 [==============================] - 19s 91ms/step - loss: 0.0018 - accuracy: 0.9993 - val_loss: 3.5454 - val_accuracy: 0.8539 Epoch 69/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 3.5949 - val_accuracy: 0.8562 Epoch 70/100 210/210 [==============================] - 19s 93ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 3.4209 - val_accuracy: 0.8529 Epoch 71/100 210/210 [==============================] - 19s 92ms/step - loss: 3.7113e-04 - accuracy: 0.9999 - val_loss: 3.5350 - val_accuracy: 0.8506 Epoch 72/100 210/210 [==============================] - 21s 99ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 3.4905 - val_accuracy: 0.8534 Epoch 73/100 210/210 [==============================] - 19s 90ms/step - loss: 2.7473e-04 - accuracy: 1.0000 - val_loss: 3.6139 - val_accuracy: 0.8534 Epoch 74/100 210/210 [==============================] - 19s 91ms/step - loss: 8.0453e-04 - accuracy: 0.9997 - val_loss: 3.6208 - val_accuracy: 0.8540 Epoch 75/100 210/210 [==============================] - 21s 102ms/step - loss: 3.7670e-04 - accuracy: 0.9999 - val_loss: 3.7896 - val_accuracy: 0.8535 Epoch 76/100 210/210 [==============================] - 19s 92ms/step - loss: 1.9838e-04 - accuracy: 1.0000 - val_loss: 3.7791 - val_accuracy: 0.8539 Epoch 77/100 210/210 [==============================] - 20s 97ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 3.5423 - val_accuracy: 0.8488 Epoch 78/100 210/210 [==============================] - 23s 110ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 3.8500 - val_accuracy: 0.8526 Epoch 79/100 210/210 [==============================] - 22s 105ms/step - loss: 3.7923e-04 - accuracy: 0.9999 - val_loss: 3.9680 - val_accuracy: 0.8546 Epoch 80/100 210/210 [==============================] - 22s 107ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 4.0722 - val_accuracy: 0.8492 Epoch 81/100 210/210 [==============================] - 21s 102ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 3.6299 - val_accuracy: 0.8528 Epoch 82/100 210/210 [==============================] - 21s 98ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 3.4745 - val_accuracy: 0.8513 Epoch 83/100 210/210 [==============================] - 20s 96ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 3.4854 - val_accuracy: 0.8541 Epoch 84/100 210/210 [==============================] - 22s 103ms/step - loss: 3.1399e-04 - accuracy: 1.0000 - val_loss: 3.6002 - val_accuracy: 0.8553 Epoch 85/100 210/210 [==============================] - 21s 102ms/step - loss: 9.3248e-05 - accuracy: 1.0000 - val_loss: 3.6709 - val_accuracy: 0.8559 Epoch 86/100 210/210 [==============================] - 20s 98ms/step - loss: 6.6062e-05 - accuracy: 1.0000 - val_loss: 3.7540 - val_accuracy: 0.8566 Epoch 87/100 210/210 [==============================] - 20s 96ms/step - loss: 5.0547e-05 - accuracy: 1.0000 - val_loss: 3.8218 - val_accuracy: 0.8561 Epoch 88/100 210/210 [==============================] - 20s 94ms/step - loss: 4.2324e-05 - accuracy: 1.0000 - val_loss: 3.8741 - val_accuracy: 0.8570 Epoch 89/100 210/210 [==============================] - 19s 93ms/step - loss: 4.2801e-05 - accuracy: 1.0000 - val_loss: 3.9727 - val_accuracy: 0.8567 Epoch 90/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 3.6109 - val_accuracy: 0.8499 Epoch 91/100 210/210 [==============================] - 20s 93ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 3.5863 - val_accuracy: 0.8538 Epoch 92/100 210/210 [==============================] - 22s 104ms/step - loss: 3.4438e-04 - accuracy: 0.9999 - val_loss: 3.6739 - val_accuracy: 0.8561 Epoch 93/100 210/210 [==============================] - 20s 94ms/step - loss: 5.3955e-04 - accuracy: 0.9998 - val_loss: 3.8243 - val_accuracy: 0.8543 Epoch 94/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 3.7420 - val_accuracy: 0.8503 Epoch 95/100 210/210 [==============================] - 20s 94ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 3.6411 - val_accuracy: 0.8538 Epoch 96/100 210/210 [==============================] - 20s 95ms/step - loss: 8.7156e-04 - accuracy: 0.9997 - val_loss: 3.6579 - val_accuracy: 0.8543 Epoch 97/100 210/210 [==============================] - 20s 94ms/step - loss: 4.6520e-04 - accuracy: 0.9998 - val_loss: 3.8132 - val_accuracy: 0.8544 Epoch 98/100 210/210 [==============================] - 20s 97ms/step - loss: 1.7340e-04 - accuracy: 1.0000 - val_loss: 3.8735 - val_accuracy: 0.8548 Epoch 99/100 210/210 [==============================] - 22s 103ms/step - loss: 7.6791e-05 - accuracy: 1.0000 - val_loss: 3.9424 - val_accuracy: 0.8546 Epoch 100/100 210/210 [==============================] - 20s 95ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 4.0380 - val_accuracy: 0.8528 563/563 [==============================] - 9s 16ms/step Epoch 1/100 210/210 [==============================] - 35s 145ms/step - loss: 0.0615 - accuracy: 0.9851 - val_loss: 1.5852 - val_accuracy: 0.8318 Epoch 2/100 210/210 [==============================] - 29s 136ms/step - loss: 0.0771 - accuracy: 0.9791 - val_loss: 1.1537 - val_accuracy: 0.8102 Epoch 3/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0830 - accuracy: 0.9785 - val_loss: 1.1608 - val_accuracy: 0.8423 Epoch 4/100 210/210 [==============================] - 29s 137ms/step - loss: 0.0566 - accuracy: 0.9849 - val_loss: 1.3572 - val_accuracy: 0.8276 Epoch 5/100 210/210 [==============================] - 29s 138ms/step - loss: 0.1046 - accuracy: 0.9738 - val_loss: 1.1518 - val_accuracy: 0.8384 Epoch 6/100 210/210 [==============================] - 33s 155ms/step - loss: 0.0747 - accuracy: 0.9797 - val_loss: 1.0984 - val_accuracy: 0.8413 Epoch 7/100 210/210 [==============================] - 30s 145ms/step - loss: 0.0635 - accuracy: 0.9838 - val_loss: 1.2823 - val_accuracy: 0.8459 Epoch 8/100 210/210 [==============================] - 30s 143ms/step - loss: 1.6468 - accuracy: 0.4547 - val_loss: 2.3036 - val_accuracy: 0.1037 Epoch 9/100 210/210 [==============================] - 30s 142ms/step - loss: 2.3045 - accuracy: 0.0991 - val_loss: 2.3032 - val_accuracy: 0.1016 Epoch 10/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3088 - accuracy: 0.1016 - val_loss: 2.3037 - val_accuracy: 0.1018 Epoch 11/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3044 - accuracy: 0.0990 - val_loss: 2.3053 - val_accuracy: 0.1002 Epoch 12/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3040 - accuracy: 0.0981 - val_loss: 2.3038 - val_accuracy: 0.1002 Epoch 13/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3039 - accuracy: 0.0993 - val_loss: 2.3029 - val_accuracy: 0.1007 Epoch 14/100 210/210 [==============================] - 30s 144ms/step - loss: 2.3039 - accuracy: 0.0978 - val_loss: 2.3037 - val_accuracy: 0.1007 Epoch 15/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3036 - accuracy: 0.1011 - val_loss: 2.3033 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3037 - accuracy: 0.0984 - val_loss: 2.3030 - val_accuracy: 0.1007 Epoch 17/100 210/210 [==============================] - 30s 142ms/step - loss: 2.3035 - accuracy: 0.1015 - val_loss: 2.3031 - val_accuracy: 0.1002 Epoch 18/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3034 - accuracy: 0.1006 - val_loss: 2.3038 - val_accuracy: 0.0982 Epoch 19/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3037 - accuracy: 0.0987 - val_loss: 2.3030 - val_accuracy: 0.1016 Epoch 20/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3034 - accuracy: 0.1000 - val_loss: 2.3038 - val_accuracy: 0.1007 Epoch 21/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3035 - accuracy: 0.1000 - val_loss: 2.3029 - val_accuracy: 0.1007 Epoch 22/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3034 - accuracy: 0.0970 - val_loss: 2.3045 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3033 - accuracy: 0.0987 - val_loss: 2.3034 - val_accuracy: 0.1016 Epoch 24/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3035 - accuracy: 0.0980 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 25/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3033 - accuracy: 0.0991 - val_loss: 2.3028 - val_accuracy: 0.1007 Epoch 26/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3032 - accuracy: 0.1016 - val_loss: 2.3034 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3034 - accuracy: 0.0965 - val_loss: 2.3028 - val_accuracy: 0.1004 Epoch 28/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3032 - accuracy: 0.0992 - val_loss: 2.3032 - val_accuracy: 0.1016 Epoch 29/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3033 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.1008 Epoch 30/100 210/210 [==============================] - 28s 136ms/step - loss: 2.3029 - accuracy: 0.1035 - val_loss: 2.3039 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3032 - accuracy: 0.1001 - val_loss: 2.3032 - val_accuracy: 0.1007 Epoch 32/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3030 - accuracy: 0.0995 - val_loss: 2.3030 - val_accuracy: 0.1002 Epoch 33/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3031 - accuracy: 0.0969 - val_loss: 2.3028 - val_accuracy: 0.1016 Epoch 34/100 210/210 [==============================] - 30s 143ms/step - loss: 2.3031 - accuracy: 0.0988 - val_loss: 2.3030 - val_accuracy: 0.1002 Epoch 35/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3030 - accuracy: 0.1006 - val_loss: 2.3027 - val_accuracy: 0.1018 Epoch 36/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3030 - accuracy: 0.0990 - val_loss: 2.3029 - val_accuracy: 0.1016 Epoch 37/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3030 - accuracy: 0.0998 - val_loss: 2.3032 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3030 - accuracy: 0.0998 - val_loss: 2.3028 - val_accuracy: 0.1007 Epoch 39/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3030 - accuracy: 0.0982 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 40/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3029 - accuracy: 0.0988 - val_loss: 2.3030 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 29s 138ms/step - loss: 2.3031 - accuracy: 0.0992 - val_loss: 2.3031 - val_accuracy: 0.1007 Epoch 42/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3031 - accuracy: 0.1012 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 43/100 210/210 [==============================] - 31s 146ms/step - loss: 2.3030 - accuracy: 0.0960 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 44/100 210/210 [==============================] - 29s 138ms/step - loss: 2.3030 - accuracy: 0.0984 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 45/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3030 - accuracy: 0.0992 - val_loss: 2.3026 - val_accuracy: 0.1007 Epoch 46/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3029 - accuracy: 0.0983 - val_loss: 2.3034 - val_accuracy: 0.0982 Epoch 47/100 210/210 [==============================] - 29s 136ms/step - loss: 2.3030 - accuracy: 0.1006 - val_loss: 2.3029 - val_accuracy: 0.1016 Epoch 48/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3030 - accuracy: 0.0990 - val_loss: 2.3031 - val_accuracy: 0.0982 Epoch 49/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3030 - accuracy: 0.1000 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 50/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3029 - accuracy: 0.0999 - val_loss: 2.3029 - val_accuracy: 0.1016 Epoch 51/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3030 - accuracy: 0.0992 - val_loss: 2.3032 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 30s 142ms/step - loss: 2.3030 - accuracy: 0.0984 - val_loss: 2.3034 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3029 - accuracy: 0.1003 - val_loss: 2.3033 - val_accuracy: 0.1007 Epoch 54/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3029 - accuracy: 0.0984 - val_loss: 2.3031 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 29s 138ms/step - loss: 2.3029 - accuracy: 0.0998 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 56/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3029 - accuracy: 0.0998 - val_loss: 2.3031 - val_accuracy: 0.0982 Epoch 57/100 210/210 [==============================] - 29s 138ms/step - loss: 2.3029 - accuracy: 0.0988 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3029 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.1016 Epoch 59/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3029 - accuracy: 0.1001 - val_loss: 2.3030 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3029 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.1018 Epoch 61/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3030 - accuracy: 0.0997 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 62/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3029 - accuracy: 0.0999 - val_loss: 2.3030 - val_accuracy: 0.1007 Epoch 63/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3029 - accuracy: 0.0987 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 64/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3029 - accuracy: 0.0984 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 65/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3029 - accuracy: 0.0980 - val_loss: 2.3030 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3028 - accuracy: 0.1009 - val_loss: 2.3029 - val_accuracy: 0.1016 Epoch 67/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3029 - accuracy: 0.0982 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3029 - accuracy: 0.0998 - val_loss: 2.3028 - val_accuracy: 0.1002 Epoch 69/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3027 - accuracy: 0.0985 - val_loss: 2.3030 - val_accuracy: 0.1007 Epoch 70/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3030 - accuracy: 0.0996 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 71/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3028 - accuracy: 0.0991 - val_loss: 2.3028 - val_accuracy: 0.1004 Epoch 72/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3029 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 73/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3028 - accuracy: 0.0987 - val_loss: 2.3029 - val_accuracy: 0.1007 Epoch 74/100 210/210 [==============================] - 30s 144ms/step - loss: 2.3029 - accuracy: 0.0996 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3028 - accuracy: 0.0995 - val_loss: 2.3028 - val_accuracy: 0.1002 Epoch 76/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3029 - accuracy: 0.0982 - val_loss: 2.3030 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 28s 133ms/step - loss: 2.3029 - accuracy: 0.0995 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3028 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 79/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3028 - accuracy: 0.0972 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3029 - accuracy: 0.0972 - val_loss: 2.3030 - val_accuracy: 0.0982 Epoch 81/100 210/210 [==============================] - 28s 131ms/step - loss: 2.3029 - accuracy: 0.0979 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 30s 141ms/step - loss: 2.3029 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3028 - accuracy: 0.0988 - val_loss: 2.3027 - val_accuracy: 0.1016 Epoch 84/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3028 - accuracy: 0.0979 - val_loss: 2.3030 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3029 - accuracy: 0.0984 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3028 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.1008 Epoch 87/100 210/210 [==============================] - 29s 138ms/step - loss: 2.3028 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 88/100 210/210 [==============================] - 29s 137ms/step - loss: 2.3028 - accuracy: 0.0996 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 30s 141ms/step - loss: 2.3028 - accuracy: 0.1007 - val_loss: 2.3029 - val_accuracy: 0.1002 Epoch 90/100 210/210 [==============================] - 28s 135ms/step - loss: 2.3029 - accuracy: 0.0983 - val_loss: 2.3028 - val_accuracy: 0.1002 Epoch 91/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3028 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3028 - accuracy: 0.1012 - val_loss: 2.3032 - val_accuracy: 0.1002 Epoch 93/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3029 - accuracy: 0.1004 - val_loss: 2.3026 - val_accuracy: 0.1007 Epoch 94/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3029 - accuracy: 0.0965 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 95/100 210/210 [==============================] - 31s 146ms/step - loss: 2.3029 - accuracy: 0.0986 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 96/100 210/210 [==============================] - 28s 132ms/step - loss: 2.3028 - accuracy: 0.0985 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 28s 134ms/step - loss: 2.3028 - accuracy: 0.0994 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 181s 867ms/step - loss: 2.3028 - accuracy: 0.1001 - val_loss: 2.3029 - val_accuracy: 0.1018 Epoch 99/100 210/210 [==============================] - 31s 146ms/step - loss: 2.3028 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.1018 Epoch 100/100 210/210 [==============================] - 29s 140ms/step - loss: 2.3029 - accuracy: 0.0992 - val_loss: 2.3029 - val_accuracy: 0.1016 563/563 [==============================] - 13s 22ms/step Epoch 1/100 210/210 [==============================] - 26s 103ms/step - loss: 2.3026 - accuracy: 0.0980 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1012 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 21s 102ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 24s 116ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 24s 115ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 22s 106ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 24s 114ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 34/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 37/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 22s 107ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 21s 102ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 563/563 [==============================] - 10s 18ms/step
print(RESULTS)
NO OF EPOCHS MODEL SCORE TRAINING ACCURACY TESTING ACCURACY 0 Adam 0.679690 0.973310 0.839389 1 RMSprop 0.659704 0.963333 0.850722 2 Adadelta 0.718556 0.997690 0.854278 3 Adagrad 0.701086 0.999738 0.855444 4 Adamax 0.692929 1.000000 0.857000 5 Nadam -0.000098 0.985143 0.845944 6 Ftrl -0.000060 0.101929 0.095500
* ADAMAX HAS REACHED 100% TRAINING ACCURACY WITH TESTING ACCURACY OF 85.7%
* ADAMAX HAS GIVEN BETTER RESULT TO THE MODEL DESIGN COMPARED TO THE SGD MODEL.
# TRY THE DESIGN WITH VARIOUS LEARNING RATES AND COMPILE
LR = np.arange(0.01,0.10,0.02)
RESULTS = pd.DataFrame()
for i in LR:
sgd = SGD(lr = i)
MODEL.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
LR_HIST = MODEL.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),epochs=100, batch_size=200).history
Y_PRED = MODEL.predict(X_TEST)
SCORE = metrics.r2_score(Y_TEST,Y_PRED)
MAX_ACC = np.max(LR_HIST['accuracy'])
MAX_VAL_ACC = np.max(LR_HIST['val_accuracy'])
RESULTS = RESULTS.append(pd.Series([i,SCORE,MAX_ACC,MAX_VAL_ACC]),ignore_index=True)
RESULTS.columns = ['LEARNING RATE','MODEL SCORE','TRAINING ACCURACY','TESTING ACCURACY']
Epoch 1/100 210/210 [==============================] - 17s 72ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 18s 84ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 17s 81ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 17s 79ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 17s 82ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 23s 109ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 34/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 37/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 21s 102ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 563/563 [==============================] - 13s 21ms/step Epoch 1/100 210/210 [==============================] - 21s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1018 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 34/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 37/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1018 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3026 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3026 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3026 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3026 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 563/563 [==============================] - 9s 15ms/step Epoch 1/100 210/210 [==============================] - 21s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 19/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 34/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1012 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 37/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1003 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 20s 98ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3026 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1014 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3026 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3026 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3026 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.0955 563/563 [==============================] - 12s 20ms/step Epoch 1/100 210/210 [==============================] - 25s 108ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 21s 102ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 8/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.0993 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0994 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0996 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1022 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.0998 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.0991 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1021 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.1004 Epoch 34/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 37/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 41/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 43/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 48/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 49/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0998 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0994 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0998 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.0987 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1021 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 100/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 563/563 [==============================] - 9s 15ms/step Epoch 1/100 210/210 [==============================] - 77s 361ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 3/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0994 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 6/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 23s 112ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 24s 114ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 9/100 210/210 [==============================] - 23s 107ms/step - loss: 2.3027 - accuracy: 0.0991 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1019 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.0999 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0999 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 22/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0985 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 30/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.0984 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 32/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 34/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0994 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3027 - accuracy: 0.1017 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 37/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0992 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 19s 93ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 40/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0986 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 41/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0993 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3027 - accuracy: 0.1000 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.0992 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 22s 107ms/step - loss: 2.3027 - accuracy: 0.0986 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1015 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1011 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3027 - accuracy: 0.1007 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 54/100 210/210 [==============================] - 24s 112ms/step - loss: 2.3027 - accuracy: 0.0995 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.1002 Epoch 57/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.0991 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1018 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 24s 112ms/step - loss: 2.3027 - accuracy: 0.0991 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1014 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3027 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1020 - val_loss: 2.3027 - val_accuracy: 0.0982 Epoch 65/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0979 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0996 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0988 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0974 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0992 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 72/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 73/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0982 - val_loss: 2.3029 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.0996 - val_loss: 2.3027 - val_accuracy: 0.1007 Epoch 77/100 210/210 [==============================] - 21s 100ms/step - loss: 2.3027 - accuracy: 0.0980 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 22s 106ms/step - loss: 2.3027 - accuracy: 0.0999 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1013 - val_loss: 2.3027 - val_accuracy: 0.1002 Epoch 80/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1012 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1004 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 23s 107ms/step - loss: 2.3027 - accuracy: 0.1021 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 84/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.0997 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 23s 109ms/step - loss: 2.3027 - accuracy: 0.1008 - val_loss: 2.3028 - val_accuracy: 0.0982 Epoch 87/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 22s 104ms/step - loss: 2.3027 - accuracy: 0.0987 - val_loss: 2.3029 - val_accuracy: 0.0982 Epoch 90/100 210/210 [==============================] - 23s 107ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3027 - accuracy: 0.1006 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3027 - accuracy: 0.1001 - val_loss: 2.3028 - val_accuracy: 0.1002 Epoch 94/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3027 - accuracy: 0.1016 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3027 - accuracy: 0.0988 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3027 - accuracy: 0.0999 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 22s 106ms/step - loss: 2.3027 - accuracy: 0.0992 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3027 - accuracy: 0.1009 - val_loss: 2.3027 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3027 - accuracy: 0.1010 - val_loss: 2.3028 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3027 - accuracy: 0.1026 - val_loss: 2.3026 - val_accuracy: 0.0982 563/563 [==============================] - 9s 16ms/step
print(RESULTS)
LEARNING RATE MODEL SCORE TRAINING ACCURACY TESTING ACCURACY 0 0.01 -0.000071 0.101929 0.095500 1 0.03 -0.000073 0.101929 0.095500 2 0.05 -0.000071 0.101929 0.098222 3 0.07 -0.000087 0.102190 0.100444 4 0.09 -0.000048 0.102595 0.100667
# OPTIMIZE THE MODEL USING LAMBDA VALUE
Lambda = 0
MODEL_L = Sequential()
MODEL_L.add(Dense(1024, kernel_initializer='normal',input_shape = (1024, )))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(256, kernel_initializer='normal'))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(512, kernel_initializer='normal'))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(256, kernel_initializer='normal'))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(128, kernel_initializer='normal'))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(64, kernel_initializer='normal'))
MODEL_L.add(Activation('relu'))
MODEL_L.add(Dense(128, kernel_initializer='normal'))
MODEL_L.add(LeakyReLU(alpha=0.1))
MODEL_L.add(Dense(128, kernel_initializer='normal'))
MODEL_L.add(LeakyReLU(alpha=0.1))
MODEL_L.add(Dense(128, kernel_initializer='normal'))
MODEL_L.add(LeakyReLU(alpha=0.1))
MODEL_L.add(Dense(10, Activation('softmax'),kernel_regularizer=regularizers.l2(Lambda)))
MODEL_L.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY = MODEL_L.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),batch_size = 200, epochs = 100, verbose = 1)
MODEL_DF = pd.DataFrame.from_dict(HISTORY.history)
Epoch 1/100 210/210 [==============================] - 24s 104ms/step - loss: 2.3026 - accuracy: 0.1018 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 2/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3025 - accuracy: 0.1021 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 3/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3025 - accuracy: 0.1020 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 4/100 210/210 [==============================] - 18s 86ms/step - loss: 2.3025 - accuracy: 0.1024 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 5/100 210/210 [==============================] - 18s 85ms/step - loss: 2.3025 - accuracy: 0.1019 - val_loss: 2.3026 - val_accuracy: 0.0955 Epoch 6/100 210/210 [==============================] - 18s 85ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 7/100 210/210 [==============================] - 18s 84ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 8/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 9/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 10/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 11/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 12/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 13/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 14/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 15/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 16/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 17/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3023 - val_accuracy: 0.0955 Epoch 18/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3023 - val_accuracy: 0.0955 Epoch 19/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3021 - accuracy: 0.1019 - val_loss: 2.3023 - val_accuracy: 0.0955 Epoch 20/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3021 - accuracy: 0.1019 - val_loss: 2.3022 - val_accuracy: 0.0955 Epoch 21/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3021 - accuracy: 0.1019 - val_loss: 2.3022 - val_accuracy: 0.0955 Epoch 22/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3020 - accuracy: 0.1019 - val_loss: 2.3021 - val_accuracy: 0.0955 Epoch 23/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3020 - accuracy: 0.1019 - val_loss: 2.3021 - val_accuracy: 0.0955 Epoch 24/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3019 - accuracy: 0.1019 - val_loss: 2.3020 - val_accuracy: 0.0955 Epoch 25/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3019 - accuracy: 0.1027 - val_loss: 2.3020 - val_accuracy: 0.0955 Epoch 26/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3019 - accuracy: 0.1020 - val_loss: 2.3020 - val_accuracy: 0.0955 Epoch 27/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3018 - accuracy: 0.1028 - val_loss: 2.3019 - val_accuracy: 0.0955 Epoch 28/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3017 - accuracy: 0.1019 - val_loss: 2.3019 - val_accuracy: 0.0955 Epoch 29/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3017 - accuracy: 0.1021 - val_loss: 2.3018 - val_accuracy: 0.0955 Epoch 30/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3016 - accuracy: 0.1019 - val_loss: 2.3017 - val_accuracy: 0.0955 Epoch 31/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3016 - accuracy: 0.1021 - val_loss: 2.3017 - val_accuracy: 0.0955 Epoch 32/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3015 - accuracy: 0.1020 - val_loss: 2.3016 - val_accuracy: 0.0955 Epoch 33/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3014 - accuracy: 0.1021 - val_loss: 2.3015 - val_accuracy: 0.0958 Epoch 34/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3014 - accuracy: 0.1021 - val_loss: 2.3015 - val_accuracy: 0.0965 Epoch 35/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3013 - accuracy: 0.1067 - val_loss: 2.3014 - val_accuracy: 0.0956 Epoch 36/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3012 - accuracy: 0.1063 - val_loss: 2.3013 - val_accuracy: 0.0956 Epoch 37/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3011 - accuracy: 0.1033 - val_loss: 2.3012 - val_accuracy: 0.0962 Epoch 38/100 210/210 [==============================] - 23s 110ms/step - loss: 2.3010 - accuracy: 0.1041 - val_loss: 2.3010 - val_accuracy: 0.0964 Epoch 39/100 210/210 [==============================] - 29s 139ms/step - loss: 2.3009 - accuracy: 0.1037 - val_loss: 2.3009 - val_accuracy: 0.1001 Epoch 40/100 210/210 [==============================] - 23s 109ms/step - loss: 2.3007 - accuracy: 0.1103 - val_loss: 2.3008 - val_accuracy: 0.1003 Epoch 41/100 210/210 [==============================] - 22s 106ms/step - loss: 2.3006 - accuracy: 0.1086 - val_loss: 2.3006 - val_accuracy: 0.1001 Epoch 42/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3004 - accuracy: 0.1033 - val_loss: 2.3004 - val_accuracy: 0.1084 Epoch 43/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3002 - accuracy: 0.1130 - val_loss: 2.3002 - val_accuracy: 0.1096 Epoch 44/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3000 - accuracy: 0.1232 - val_loss: 2.3000 - val_accuracy: 0.1027 Epoch 45/100 210/210 [==============================] - 22s 106ms/step - loss: 2.2997 - accuracy: 0.1087 - val_loss: 2.2997 - val_accuracy: 0.1123 Epoch 46/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2994 - accuracy: 0.1172 - val_loss: 2.2994 - val_accuracy: 0.1153 Epoch 47/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2991 - accuracy: 0.1191 - val_loss: 2.2990 - val_accuracy: 0.1164 Epoch 48/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2987 - accuracy: 0.1264 - val_loss: 2.2986 - val_accuracy: 0.1143 Epoch 49/100 210/210 [==============================] - 22s 103ms/step - loss: 2.2982 - accuracy: 0.1268 - val_loss: 2.2980 - val_accuracy: 0.1159 Epoch 50/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2976 - accuracy: 0.1297 - val_loss: 2.2974 - val_accuracy: 0.1162 Epoch 51/100 210/210 [==============================] - 22s 103ms/step - loss: 2.2969 - accuracy: 0.1250 - val_loss: 2.2966 - val_accuracy: 0.1244 Epoch 52/100 210/210 [==============================] - 22s 103ms/step - loss: 2.2960 - accuracy: 0.1360 - val_loss: 2.2956 - val_accuracy: 0.1301 Epoch 53/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2949 - accuracy: 0.1436 - val_loss: 2.2944 - val_accuracy: 0.1296 Epoch 54/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2935 - accuracy: 0.1403 - val_loss: 2.2926 - val_accuracy: 0.1429 Epoch 55/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2915 - accuracy: 0.1568 - val_loss: 2.2903 - val_accuracy: 0.1589 Epoch 56/100 210/210 [==============================] - 22s 104ms/step - loss: 2.2887 - accuracy: 0.1627 - val_loss: 2.2868 - val_accuracy: 0.1828 Epoch 57/100 210/210 [==============================] - 21s 102ms/step - loss: 2.2844 - accuracy: 0.1899 - val_loss: 2.2815 - val_accuracy: 0.1882 Epoch 58/100 210/210 [==============================] - 22s 106ms/step - loss: 2.2773 - accuracy: 0.1892 - val_loss: 2.2720 - val_accuracy: 0.2101 Epoch 59/100 210/210 [==============================] - 21s 102ms/step - loss: 2.2638 - accuracy: 0.2000 - val_loss: 2.2529 - val_accuracy: 0.2083 Epoch 60/100 210/210 [==============================] - 21s 101ms/step - loss: 2.2350 - accuracy: 0.2041 - val_loss: 2.2111 - val_accuracy: 0.2001 Epoch 61/100 210/210 [==============================] - 21s 101ms/step - loss: 2.1933 - accuracy: 0.1867 - val_loss: 2.2043 - val_accuracy: 0.1593 Epoch 62/100 210/210 [==============================] - 21s 101ms/step - loss: 2.1655 - accuracy: 0.1718 - val_loss: 2.2917 - val_accuracy: 0.1260 Epoch 63/100 210/210 [==============================] - 21s 101ms/step - loss: 2.1435 - accuracy: 0.1765 - val_loss: 2.0765 - val_accuracy: 0.2138 Epoch 64/100 210/210 [==============================] - 22s 103ms/step - loss: 2.1201 - accuracy: 0.1833 - val_loss: 2.1496 - val_accuracy: 0.1718 Epoch 65/100 210/210 [==============================] - 24s 116ms/step - loss: 2.1000 - accuracy: 0.1904 - val_loss: 2.0485 - val_accuracy: 0.2055 Epoch 66/100 210/210 [==============================] - 26s 125ms/step - loss: 2.0724 - accuracy: 0.1961 - val_loss: 2.0462 - val_accuracy: 0.1997 Epoch 67/100 210/210 [==============================] - 6327s 30s/step - loss: 2.0592 - accuracy: 0.1998 - val_loss: 2.0233 - val_accuracy: 0.2094 Epoch 68/100 210/210 [==============================] - 23s 112ms/step - loss: 2.0418 - accuracy: 0.2121 - val_loss: 2.0038 - val_accuracy: 0.2277 Epoch 69/100 210/210 [==============================] - 21s 99ms/step - loss: 2.0272 - accuracy: 0.2191 - val_loss: 2.2766 - val_accuracy: 0.1382 Epoch 70/100 210/210 [==============================] - 19s 89ms/step - loss: 2.0077 - accuracy: 0.2267 - val_loss: 1.9574 - val_accuracy: 0.2741 Epoch 71/100 210/210 [==============================] - 18s 84ms/step - loss: 1.9786 - accuracy: 0.2440 - val_loss: 2.0205 - val_accuracy: 0.2216 Epoch 72/100 210/210 [==============================] - 16s 78ms/step - loss: 1.9567 - accuracy: 0.2495 - val_loss: 1.8882 - val_accuracy: 0.2949 Epoch 73/100 210/210 [==============================] - 16s 78ms/step - loss: 1.9143 - accuracy: 0.2720 - val_loss: 1.8669 - val_accuracy: 0.2835 Epoch 74/100 210/210 [==============================] - 17s 81ms/step - loss: 1.8797 - accuracy: 0.2856 - val_loss: 1.8676 - val_accuracy: 0.2932 Epoch 75/100 210/210 [==============================] - 18s 84ms/step - loss: 1.8365 - accuracy: 0.3074 - val_loss: 1.7870 - val_accuracy: 0.3334 Epoch 76/100 210/210 [==============================] - 17s 81ms/step - loss: 1.8013 - accuracy: 0.3385 - val_loss: 1.7234 - val_accuracy: 0.3799 Epoch 77/100 210/210 [==============================] - 17s 79ms/step - loss: 1.7425 - accuracy: 0.3636 - val_loss: 1.6735 - val_accuracy: 0.3948 Epoch 78/100 210/210 [==============================] - 19s 92ms/step - loss: 1.6866 - accuracy: 0.3853 - val_loss: 1.6019 - val_accuracy: 0.4277 Epoch 79/100 210/210 [==============================] - 18s 84ms/step - loss: 1.6496 - accuracy: 0.3988 - val_loss: 1.5724 - val_accuracy: 0.4323 Epoch 80/100 210/210 [==============================] - 18s 86ms/step - loss: 1.6262 - accuracy: 0.4054 - val_loss: 1.5478 - val_accuracy: 0.4447 Epoch 81/100 210/210 [==============================] - 19s 90ms/step - loss: 1.5960 - accuracy: 0.4205 - val_loss: 1.5936 - val_accuracy: 0.4130 Epoch 82/100 210/210 [==============================] - 17s 82ms/step - loss: 1.5716 - accuracy: 0.4302 - val_loss: 1.4914 - val_accuracy: 0.4604 Epoch 83/100 210/210 [==============================] - 19s 90ms/step - loss: 1.5477 - accuracy: 0.4351 - val_loss: 1.5145 - val_accuracy: 0.4528 Epoch 84/100 210/210 [==============================] - 18s 86ms/step - loss: 1.5230 - accuracy: 0.4483 - val_loss: 1.4591 - val_accuracy: 0.4755 Epoch 85/100 210/210 [==============================] - 18s 84ms/step - loss: 1.5061 - accuracy: 0.4545 - val_loss: 1.4916 - val_accuracy: 0.4739 Epoch 86/100 210/210 [==============================] - 22s 104ms/step - loss: 1.4738 - accuracy: 0.4712 - val_loss: 1.4280 - val_accuracy: 0.4974 Epoch 87/100 210/210 [==============================] - 21s 99ms/step - loss: 1.4492 - accuracy: 0.4792 - val_loss: 1.4022 - val_accuracy: 0.4909 Epoch 88/100 210/210 [==============================] - 18s 87ms/step - loss: 1.4214 - accuracy: 0.4932 - val_loss: 1.4171 - val_accuracy: 0.4944 Epoch 89/100 210/210 [==============================] - 18s 85ms/step - loss: 1.3952 - accuracy: 0.5040 - val_loss: 1.4996 - val_accuracy: 0.4470 Epoch 90/100 210/210 [==============================] - 18s 87ms/step - loss: 1.3668 - accuracy: 0.5160 - val_loss: 1.3263 - val_accuracy: 0.5307 Epoch 91/100 210/210 [==============================] - 19s 89ms/step - loss: 1.3355 - accuracy: 0.5328 - val_loss: 1.2818 - val_accuracy: 0.5612 Epoch 92/100 210/210 [==============================] - 18s 84ms/step - loss: 1.3103 - accuracy: 0.5393 - val_loss: 1.2700 - val_accuracy: 0.5628 Epoch 93/100 210/210 [==============================] - 19s 88ms/step - loss: 1.2868 - accuracy: 0.5544 - val_loss: 1.3763 - val_accuracy: 0.5239 Epoch 94/100 210/210 [==============================] - 18s 86ms/step - loss: 1.2473 - accuracy: 0.5746 - val_loss: 1.2739 - val_accuracy: 0.5521 Epoch 95/100 210/210 [==============================] - 18s 88ms/step - loss: 1.2317 - accuracy: 0.5797 - val_loss: 1.2052 - val_accuracy: 0.5982 Epoch 96/100 210/210 [==============================] - 18s 85ms/step - loss: 1.1947 - accuracy: 0.5972 - val_loss: 1.2003 - val_accuracy: 0.6044 Epoch 97/100 210/210 [==============================] - 19s 92ms/step - loss: 1.1583 - accuracy: 0.6100 - val_loss: 1.3163 - val_accuracy: 0.5509 Epoch 98/100 210/210 [==============================] - 20s 93ms/step - loss: 1.1262 - accuracy: 0.6262 - val_loss: 1.1489 - val_accuracy: 0.6267 Epoch 99/100 210/210 [==============================] - 18s 88ms/step - loss: 1.0913 - accuracy: 0.6400 - val_loss: 1.0915 - val_accuracy: 0.6451 Epoch 100/100 210/210 [==============================] - 18s 87ms/step - loss: 1.0556 - accuracy: 0.6494 - val_loss: 1.2948 - val_accuracy: 0.5766
Y_PRED = MODEL_L.predict(X_TEST)
print('MODEL SCORE: ', metrics.r2_score(Y_TEST,Y_PRED))
print('\n\n',MODEL_DF.sort_values('accuracy',ascending = False).head())
563/563 [==============================] - 8s 13ms/step
MODEL SCORE: 0.3905541473800947
loss accuracy val_loss val_accuracy
99 1.055579 0.649429 1.294794 0.576556
98 1.091349 0.639976 1.091500 0.645056
97 1.126152 0.626238 1.148851 0.626667
96 1.158335 0.610000 1.316288 0.550944
95 1.194714 0.597214 1.200253 0.604444
# OPTIMIZE THE MODEL USING LAMBDA VALUE AT 1e3
Lambda = 1e3
MODEL_L1 = Sequential()
MODEL_L1.add(Dense(1024, kernel_initializer='normal',input_shape = (1024, )))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(256, kernel_initializer='normal'))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(512, kernel_initializer='normal'))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(256, kernel_initializer='normal'))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(128, kernel_initializer='normal'))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(64, kernel_initializer='normal'))
MODEL_L1.add(Activation('relu'))
MODEL_L1.add(Dense(128, kernel_initializer='normal'))
MODEL_L1.add(LeakyReLU(alpha=0.1))
MODEL_L1.add(Dense(128, kernel_initializer='normal'))
MODEL_L1.add(LeakyReLU(alpha=0.1))
MODEL_L1.add(Dense(128, kernel_initializer='normal'))
MODEL_L1.add(LeakyReLU(alpha=0.1))
MODEL_L1.add(Dense(10, Activation('softmax'),kernel_regularizer=regularizers.l2(Lambda)))
MODEL_L1.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY = MODEL_L1.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),batch_size = 200, epochs = 100, verbose = 1)
MODEL_DF = pd.DataFrame.from_dict(HISTORY.history)
Epoch 1/100 210/210 [==============================] - 16s 72ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 2/100 210/210 [==============================] - 17s 80ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 3/100 210/210 [==============================] - 17s 79ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 4/100 210/210 [==============================] - 17s 81ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 5/100 210/210 [==============================] - 17s 80ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 6/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 7/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 8/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 9/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 10/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 11/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 12/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 13/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 14/100 210/210 [==============================] - 19s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 15/100 210/210 [==============================] - 20s 94ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 16/100 210/210 [==============================] - 19s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 17/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 18/100 210/210 [==============================] - 19s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 19/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 20/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 21/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 22/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 23/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 24/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 25/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 26/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 27/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 28/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 29/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 30/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 31/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 32/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 33/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 34/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 35/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 36/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 37/100 210/210 [==============================] - 20s 93ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 38/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 39/100 210/210 [==============================] - 20s 96ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 40/100 210/210 [==============================] - 19s 92ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 41/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 42/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 43/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 44/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 45/100 210/210 [==============================] - 19s 92ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 46/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 47/100 210/210 [==============================] - 21s 99ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 48/100 210/210 [==============================] - 20s 95ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 49/100 210/210 [==============================] - 20s 93ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 50/100 210/210 [==============================] - 20s 94ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 51/100 210/210 [==============================] - 21s 98ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 52/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 53/100 210/210 [==============================] - 19s 92ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 54/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 55/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 56/100 210/210 [==============================] - 19s 92ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 57/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 58/100 210/210 [==============================] - 20s 93ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 59/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 60/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 61/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 62/100 210/210 [==============================] - 21s 101ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 63/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 64/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 65/100 210/210 [==============================] - 18s 86ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 66/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 67/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 68/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 69/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 70/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 71/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 72/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 73/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 74/100 210/210 [==============================] - 18s 87ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 75/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 76/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 77/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 78/100 210/210 [==============================] - 20s 93ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 79/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 80/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 81/100 210/210 [==============================] - 19s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 82/100 210/210 [==============================] - 20s 94ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 83/100 210/210 [==============================] - 22s 105ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 84/100 210/210 [==============================] - 20s 97ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 85/100 210/210 [==============================] - 19s 92ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 86/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 87/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 88/100 210/210 [==============================] - 19s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 89/100 210/210 [==============================] - 18s 88ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 90/100 210/210 [==============================] - 19s 91ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 91/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 92/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 93/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 94/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 95/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 96/100 210/210 [==============================] - 19s 89ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 97/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 98/100 210/210 [==============================] - 20s 95ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 99/100 210/210 [==============================] - 19s 90ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008 Epoch 100/100 210/210 [==============================] - 20s 95ms/step - loss: nan - accuracy: 0.0997 - val_loss: nan - val_accuracy: 0.1008
# REDUCING THE LEARNING RATE AND COMPILE THE INITIAL MODEL.
sgd = SGD(lr = 0.001)
MODEL.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
HISTORY = MODEL.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),batch_size = 200, epochs = 100, verbose = 1)
MODEL_DF = pd.DataFrame.from_dict(HISTORY.history)
Epoch 1/100 210/210 [==============================] - 16s 69ms/step - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3025 - val_accuracy: 0.1014 Epoch 2/100 210/210 [==============================] - 18s 83ms/step - loss: 2.3026 - accuracy: 0.1007 - val_loss: 2.3025 - val_accuracy: 0.1016 Epoch 3/100 210/210 [==============================] - 17s 81ms/step - loss: 2.3025 - accuracy: 0.1000 - val_loss: 2.3025 - val_accuracy: 0.1026 Epoch 4/100 210/210 [==============================] - 17s 81ms/step - loss: 2.3025 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.1032 Epoch 5/100 210/210 [==============================] - 18s 85ms/step - loss: 2.3025 - accuracy: 0.1033 - val_loss: 2.3025 - val_accuracy: 0.1044 Epoch 6/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3025 - accuracy: 0.1041 - val_loss: 2.3025 - val_accuracy: 0.1063 Epoch 7/100 210/210 [==============================] - 18s 86ms/step - loss: 2.3025 - accuracy: 0.1046 - val_loss: 2.3025 - val_accuracy: 0.1057 Epoch 8/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3025 - accuracy: 0.1045 - val_loss: 2.3025 - val_accuracy: 0.1036 Epoch 9/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3025 - accuracy: 0.1023 - val_loss: 2.3025 - val_accuracy: 0.1006 Epoch 10/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3025 - accuracy: 0.1014 - val_loss: 2.3025 - val_accuracy: 0.0982 Epoch 11/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3025 - accuracy: 0.1016 - val_loss: 2.3025 - val_accuracy: 0.0953 Epoch 12/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3025 - accuracy: 0.1003 - val_loss: 2.3025 - val_accuracy: 0.0944 Epoch 13/100 210/210 [==============================] - 20s 94ms/step - loss: 2.3025 - accuracy: 0.1006 - val_loss: 2.3025 - val_accuracy: 0.0945 Epoch 14/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3025 - accuracy: 0.0998 - val_loss: 2.3025 - val_accuracy: 0.0947 Epoch 15/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3025 - accuracy: 0.1000 - val_loss: 2.3025 - val_accuracy: 0.0936 Epoch 16/100 210/210 [==============================] - 22s 103ms/step - loss: 2.3025 - accuracy: 0.1006 - val_loss: 2.3025 - val_accuracy: 0.0936 Epoch 17/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3025 - accuracy: 0.1009 - val_loss: 2.3025 - val_accuracy: 0.0939 Epoch 18/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3025 - accuracy: 0.1014 - val_loss: 2.3025 - val_accuracy: 0.0943 Epoch 19/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3024 - accuracy: 0.1012 - val_loss: 2.3025 - val_accuracy: 0.0949 Epoch 20/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3024 - accuracy: 0.1015 - val_loss: 2.3025 - val_accuracy: 0.0954 Epoch 21/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1015 - val_loss: 2.3025 - val_accuracy: 0.0954 Epoch 22/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3024 - accuracy: 0.1011 - val_loss: 2.3025 - val_accuracy: 0.0953 Epoch 23/100 210/210 [==============================] - 18s 86ms/step - loss: 2.3024 - accuracy: 0.1014 - val_loss: 2.3025 - val_accuracy: 0.0954 Epoch 24/100 210/210 [==============================] - 17s 82ms/step - loss: 2.3024 - accuracy: 0.1016 - val_loss: 2.3025 - val_accuracy: 0.0953 Epoch 25/100 210/210 [==============================] - 17s 81ms/step - loss: 2.3024 - accuracy: 0.1014 - val_loss: 2.3025 - val_accuracy: 0.0957 Epoch 26/100 210/210 [==============================] - 17s 81ms/step - loss: 2.3024 - accuracy: 0.1014 - val_loss: 2.3025 - val_accuracy: 0.0958 Epoch 27/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3024 - accuracy: 0.1018 - val_loss: 2.3025 - val_accuracy: 0.0957 Epoch 28/100 210/210 [==============================] - 18s 86ms/step - loss: 2.3024 - accuracy: 0.1016 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 29/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3024 - accuracy: 0.1018 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 30/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 31/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 32/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 33/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0956 Epoch 34/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 35/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 36/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 37/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 38/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 39/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 40/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 41/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 42/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 43/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 44/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 45/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 46/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 47/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 48/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 49/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3024 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 50/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 51/100 210/210 [==============================] - 21s 98ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 52/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 53/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 54/100 210/210 [==============================] - 23s 108ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 55/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 56/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 57/100 210/210 [==============================] - 18s 87ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 58/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 59/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 60/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 61/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 62/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 63/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 64/100 210/210 [==============================] - 24s 112ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 65/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 66/100 210/210 [==============================] - 22s 105ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 67/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 68/100 210/210 [==============================] - 20s 93ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 69/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 70/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3025 - val_accuracy: 0.0955 Epoch 71/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 72/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 73/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 74/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 75/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 76/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 77/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 78/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 79/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 80/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 81/100 210/210 [==============================] - 19s 88ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 82/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 83/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 84/100 210/210 [==============================] - 20s 97ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 85/100 210/210 [==============================] - 19s 92ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 86/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 87/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 88/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3023 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 89/100 210/210 [==============================] - 18s 88ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 90/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 91/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 92/100 210/210 [==============================] - 20s 95ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 93/100 210/210 [==============================] - 20s 96ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 94/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 95/100 210/210 [==============================] - 19s 90ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 96/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 97/100 210/210 [==============================] - 21s 99ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 98/100 210/210 [==============================] - 19s 89ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 99/100 210/210 [==============================] - 21s 101ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955 Epoch 100/100 210/210 [==============================] - 19s 91ms/step - loss: 2.3022 - accuracy: 0.1019 - val_loss: 2.3024 - val_accuracy: 0.0955
Y_PRED = MODEL.predict(X_TEST)
print('MODEL SCORE: ', metrics.r2_score(Y_TEST,Y_PRED))
print('\n\n',MODEL_DF.sort_values('accuracy',ascending = False).head())
563/563 [==============================] - 7s 12ms/step
MODEL SCORE: 6.5529116768159316e-06
loss accuracy val_loss val_accuracy
6 2.302511 0.104571 2.302523 0.105722
7 2.302504 0.104500 2.302523 0.103611
5 2.302517 0.104095 2.302524 0.106278
4 2.302523 0.103286 2.302525 0.104444
8 2.302498 0.102333 2.302522 0.100556
# BUILD THE FINAL MODEL
MODEL_FIN = Sequential()
MODEL_FIN.add(Dense(1024, kernel_initializer='normal',input_shape = (1024, )))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(256, kernel_initializer='normal'))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(512, kernel_initializer='normal'))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(256, kernel_initializer='normal'))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(128, kernel_initializer='normal'))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(64, kernel_initializer='normal'))
MODEL_FIN.add(Activation('relu'))
MODEL_FIN.add(Dense(128, kernel_initializer='normal'))
MODEL_FIN.add(LeakyReLU(alpha=0.1))
MODEL_FIN.add(Dense(128, kernel_initializer='normal'))
MODEL_FIN.add(LeakyReLU(alpha=0.1))
MODEL_FIN.add(Dense(128, kernel_initializer='normal'))
MODEL_FIN.add(LeakyReLU(alpha=0.1))
MODEL_FIN.add(Dense(10))
MODEL_FIN.add(Activation('softmax'))
MODEL_FIN.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
HISTORY = MODEL_FIN.fit(X_TRAIN, Y_TRAIN, validation_data=(X_TEST,Y_TEST),batch_size = 100, epochs = 100, verbose = 1)
Epoch 1/100 420/420 [==============================] - 24s 53ms/step - loss: 0.8439 - accuracy: 0.7216 - val_loss: 0.8498 - val_accuracy: 0.7336 Epoch 2/100 420/420 [==============================] - 23s 54ms/step - loss: 0.8193 - accuracy: 0.7326 - val_loss: 0.8315 - val_accuracy: 0.7332 Epoch 3/100 420/420 [==============================] - 23s 55ms/step - loss: 0.7872 - accuracy: 0.7423 - val_loss: 0.8618 - val_accuracy: 0.7254 Epoch 4/100 420/420 [==============================] - 24s 56ms/step - loss: 0.7495 - accuracy: 0.7561 - val_loss: 1.0163 - val_accuracy: 0.6712 Epoch 5/100 420/420 [==============================] - 22s 53ms/step - loss: 0.7362 - accuracy: 0.7619 - val_loss: 0.7932 - val_accuracy: 0.7524 Epoch 6/100 420/420 [==============================] - 23s 54ms/step - loss: 0.7173 - accuracy: 0.7684 - val_loss: 0.8473 - val_accuracy: 0.7359 Epoch 7/100 420/420 [==============================] - 22s 51ms/step - loss: 0.7042 - accuracy: 0.7733 - val_loss: 0.8031 - val_accuracy: 0.7467 Epoch 8/100 420/420 [==============================] - 25s 60ms/step - loss: 0.6842 - accuracy: 0.7775 - val_loss: 0.8246 - val_accuracy: 0.7414 Epoch 9/100 420/420 [==============================] - 25s 60ms/step - loss: 0.6612 - accuracy: 0.7880 - val_loss: 0.8367 - val_accuracy: 0.7308 Epoch 10/100 420/420 [==============================] - 25s 60ms/step - loss: 0.6473 - accuracy: 0.7911 - val_loss: 0.7780 - val_accuracy: 0.7587 Epoch 11/100 420/420 [==============================] - 26s 62ms/step - loss: 0.6406 - accuracy: 0.7926 - val_loss: 0.7725 - val_accuracy: 0.7597 Epoch 12/100 420/420 [==============================] - 25s 60ms/step - loss: 0.6149 - accuracy: 0.8015 - val_loss: 0.8058 - val_accuracy: 0.7471 Epoch 13/100 420/420 [==============================] - 28s 66ms/step - loss: 0.6067 - accuracy: 0.8042 - val_loss: 0.7299 - val_accuracy: 0.7722 Epoch 14/100 420/420 [==============================] - 26s 62ms/step - loss: 0.5940 - accuracy: 0.8070 - val_loss: 0.7461 - val_accuracy: 0.7716 Epoch 15/100 420/420 [==============================] - 26s 61ms/step - loss: 0.5805 - accuracy: 0.8142 - val_loss: 0.7555 - val_accuracy: 0.7635 Epoch 16/100 420/420 [==============================] - 26s 62ms/step - loss: 0.5697 - accuracy: 0.8150 - val_loss: 0.7409 - val_accuracy: 0.7700 Epoch 17/100 420/420 [==============================] - 27s 63ms/step - loss: 0.5574 - accuracy: 0.8215 - val_loss: 0.7905 - val_accuracy: 0.7586 Epoch 18/100 420/420 [==============================] - 26s 62ms/step - loss: 0.5511 - accuracy: 0.8219 - val_loss: 0.7865 - val_accuracy: 0.7562 Epoch 19/100 420/420 [==============================] - 27s 63ms/step - loss: 0.5403 - accuracy: 0.8241 - val_loss: 0.7838 - val_accuracy: 0.7583 Epoch 20/100 420/420 [==============================] - 26s 61ms/step - loss: 0.5290 - accuracy: 0.8292 - val_loss: 0.7644 - val_accuracy: 0.7682 Epoch 21/100 420/420 [==============================] - 26s 61ms/step - loss: 0.5142 - accuracy: 0.8334 - val_loss: 0.9032 - val_accuracy: 0.7327 Epoch 22/100 420/420 [==============================] - 26s 61ms/step - loss: 0.4994 - accuracy: 0.8405 - val_loss: 0.7343 - val_accuracy: 0.7776 Epoch 23/100 420/420 [==============================] - 26s 62ms/step - loss: 0.5085 - accuracy: 0.8349 - val_loss: 0.7721 - val_accuracy: 0.7726 Epoch 24/100 420/420 [==============================] - 26s 63ms/step - loss: 0.4881 - accuracy: 0.8420 - val_loss: 0.6889 - val_accuracy: 0.7945 Epoch 25/100 420/420 [==============================] - 26s 61ms/step - loss: 0.4811 - accuracy: 0.8435 - val_loss: 0.7049 - val_accuracy: 0.7886 Epoch 26/100 420/420 [==============================] - 26s 63ms/step - loss: 0.4759 - accuracy: 0.8463 - val_loss: 0.7122 - val_accuracy: 0.7842 Epoch 27/100 420/420 [==============================] - 27s 65ms/step - loss: 0.4600 - accuracy: 0.8528 - val_loss: 0.7431 - val_accuracy: 0.7746 Epoch 28/100 420/420 [==============================] - 31s 74ms/step - loss: 0.4533 - accuracy: 0.8538 - val_loss: 0.7575 - val_accuracy: 0.7733 Epoch 29/100 420/420 [==============================] - 30s 70ms/step - loss: 0.4412 - accuracy: 0.8572 - val_loss: 0.7262 - val_accuracy: 0.7825 Epoch 30/100 420/420 [==============================] - 24s 58ms/step - loss: 0.4403 - accuracy: 0.8561 - val_loss: 0.7230 - val_accuracy: 0.7939 Epoch 31/100 420/420 [==============================] - 24s 58ms/step - loss: 0.4275 - accuracy: 0.8626 - val_loss: 0.7286 - val_accuracy: 0.7885 Epoch 32/100 420/420 [==============================] - 23s 54ms/step - loss: 0.4261 - accuracy: 0.8621 - val_loss: 0.7321 - val_accuracy: 0.7867 Epoch 33/100 420/420 [==============================] - 23s 55ms/step - loss: 0.4084 - accuracy: 0.8671 - val_loss: 0.7825 - val_accuracy: 0.7642 Epoch 34/100 420/420 [==============================] - 23s 54ms/step - loss: 0.4089 - accuracy: 0.8672 - val_loss: 0.7255 - val_accuracy: 0.7901 Epoch 35/100 420/420 [==============================] - 26s 62ms/step - loss: 0.4091 - accuracy: 0.8667 - val_loss: 0.7345 - val_accuracy: 0.7911 Epoch 36/100 420/420 [==============================] - 25s 60ms/step - loss: 0.3915 - accuracy: 0.8741 - val_loss: 0.7194 - val_accuracy: 0.8020 Epoch 37/100 420/420 [==============================] - 26s 62ms/step - loss: 0.3878 - accuracy: 0.8752 - val_loss: 0.7412 - val_accuracy: 0.7964 Epoch 38/100 420/420 [==============================] - 26s 62ms/step - loss: 0.3774 - accuracy: 0.8786 - val_loss: 0.7165 - val_accuracy: 0.8023 Epoch 39/100 420/420 [==============================] - 26s 63ms/step - loss: 0.3785 - accuracy: 0.8780 - val_loss: 0.7758 - val_accuracy: 0.7809 Epoch 40/100 420/420 [==============================] - 26s 62ms/step - loss: 0.3633 - accuracy: 0.8840 - val_loss: 0.7094 - val_accuracy: 0.8052 Epoch 41/100 420/420 [==============================] - 27s 65ms/step - loss: 0.3703 - accuracy: 0.8808 - val_loss: 0.7013 - val_accuracy: 0.8016 Epoch 42/100 420/420 [==============================] - 29s 68ms/step - loss: 0.3396 - accuracy: 0.8915 - val_loss: 0.7310 - val_accuracy: 0.8007 Epoch 43/100 420/420 [==============================] - 31s 75ms/step - loss: 0.3412 - accuracy: 0.8881 - val_loss: 0.7574 - val_accuracy: 0.7908 Epoch 44/100 420/420 [==============================] - 28s 67ms/step - loss: 0.3381 - accuracy: 0.8908 - val_loss: 0.7387 - val_accuracy: 0.7928 Epoch 45/100 420/420 [==============================] - 27s 64ms/step - loss: 0.3415 - accuracy: 0.8903 - val_loss: 0.7848 - val_accuracy: 0.7814 Epoch 46/100 420/420 [==============================] - 28s 67ms/step - loss: 0.3321 - accuracy: 0.8930 - val_loss: 0.7241 - val_accuracy: 0.8052 Epoch 47/100 420/420 [==============================] - 27s 65ms/step - loss: 0.3167 - accuracy: 0.8981 - val_loss: 0.8557 - val_accuracy: 0.7793 Epoch 48/100 420/420 [==============================] - 26s 61ms/step - loss: 0.3090 - accuracy: 0.9005 - val_loss: 0.7710 - val_accuracy: 0.7971 Epoch 49/100 420/420 [==============================] - 26s 62ms/step - loss: 0.3003 - accuracy: 0.9018 - val_loss: 0.7804 - val_accuracy: 0.8007 Epoch 50/100 420/420 [==============================] - 28s 67ms/step - loss: 0.3018 - accuracy: 0.9025 - val_loss: 0.7654 - val_accuracy: 0.7930 Epoch 51/100 420/420 [==============================] - 27s 64ms/step - loss: 0.3054 - accuracy: 0.9021 - val_loss: 0.7775 - val_accuracy: 0.7980 Epoch 52/100 420/420 [==============================] - 27s 63ms/step - loss: 0.2871 - accuracy: 0.9076 - val_loss: 0.7729 - val_accuracy: 0.8068 Epoch 53/100 420/420 [==============================] - 28s 66ms/step - loss: 0.2893 - accuracy: 0.9061 - val_loss: 0.7994 - val_accuracy: 0.7889 Epoch 54/100 420/420 [==============================] - 26s 62ms/step - loss: 0.2724 - accuracy: 0.9135 - val_loss: 0.7865 - val_accuracy: 0.8001 Epoch 55/100 420/420 [==============================] - 28s 67ms/step - loss: 0.2833 - accuracy: 0.9088 - val_loss: 0.7586 - val_accuracy: 0.8169 Epoch 56/100 420/420 [==============================] - 26s 61ms/step - loss: 0.2712 - accuracy: 0.9123 - val_loss: 0.7585 - val_accuracy: 0.8052 Epoch 57/100 420/420 [==============================] - 27s 65ms/step - loss: 0.2625 - accuracy: 0.9147 - val_loss: 0.8333 - val_accuracy: 0.7914 Epoch 58/100 420/420 [==============================] - 27s 65ms/step - loss: 0.2642 - accuracy: 0.9150 - val_loss: 0.7920 - val_accuracy: 0.8040 Epoch 59/100 420/420 [==============================] - 27s 63ms/step - loss: 0.2574 - accuracy: 0.9168 - val_loss: 0.7888 - val_accuracy: 0.8071 Epoch 60/100 420/420 [==============================] - 26s 62ms/step - loss: 0.2607 - accuracy: 0.9175 - val_loss: 0.7621 - val_accuracy: 0.8164 Epoch 61/100 420/420 [==============================] - 26s 62ms/step - loss: 0.2485 - accuracy: 0.9205 - val_loss: 0.8498 - val_accuracy: 0.7919 Epoch 62/100 420/420 [==============================] - 27s 64ms/step - loss: 0.2342 - accuracy: 0.9260 - val_loss: 0.8267 - val_accuracy: 0.8003 Epoch 63/100 420/420 [==============================] - 27s 64ms/step - loss: 0.2329 - accuracy: 0.9243 - val_loss: 0.8324 - val_accuracy: 0.8016 Epoch 64/100 420/420 [==============================] - 26s 63ms/step - loss: 0.2335 - accuracy: 0.9241 - val_loss: 0.8544 - val_accuracy: 0.8016 Epoch 65/100 420/420 [==============================] - 26s 62ms/step - loss: 0.2466 - accuracy: 0.9211 - val_loss: 0.8051 - val_accuracy: 0.8082 Epoch 66/100 420/420 [==============================] - 26s 63ms/step - loss: 0.2209 - accuracy: 0.9286 - val_loss: 0.8288 - val_accuracy: 0.7941 Epoch 67/100 420/420 [==============================] - 26s 62ms/step - loss: 0.2214 - accuracy: 0.9285 - val_loss: 0.8442 - val_accuracy: 0.8001 Epoch 68/100 420/420 [==============================] - 27s 65ms/step - loss: 0.2110 - accuracy: 0.9314 - val_loss: 0.8407 - val_accuracy: 0.8049 Epoch 69/100 420/420 [==============================] - 27s 65ms/step - loss: 0.2162 - accuracy: 0.9297 - val_loss: 0.8615 - val_accuracy: 0.7960 Epoch 70/100 420/420 [==============================] - 28s 67ms/step - loss: 0.2095 - accuracy: 0.9321 - val_loss: 0.8590 - val_accuracy: 0.8023 Epoch 71/100 420/420 [==============================] - 29s 68ms/step - loss: 0.2094 - accuracy: 0.9318 - val_loss: 0.9505 - val_accuracy: 0.7778 Epoch 72/100 420/420 [==============================] - 26s 63ms/step - loss: 0.2104 - accuracy: 0.9323 - val_loss: 0.8435 - val_accuracy: 0.8083 Epoch 73/100 420/420 [==============================] - 27s 64ms/step - loss: 0.2174 - accuracy: 0.9290 - val_loss: 0.8317 - val_accuracy: 0.8112 Epoch 74/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1897 - accuracy: 0.9388 - val_loss: 1.2567 - val_accuracy: 0.7516 Epoch 75/100 420/420 [==============================] - 26s 63ms/step - loss: 0.2045 - accuracy: 0.9356 - val_loss: 0.8540 - val_accuracy: 0.8032 Epoch 76/100 420/420 [==============================] - 27s 63ms/step - loss: 0.1918 - accuracy: 0.9367 - val_loss: 0.8311 - val_accuracy: 0.8139 Epoch 77/100 420/420 [==============================] - 26s 61ms/step - loss: 0.1777 - accuracy: 0.9432 - val_loss: 0.8636 - val_accuracy: 0.8121 Epoch 78/100 420/420 [==============================] - 27s 64ms/step - loss: 0.2024 - accuracy: 0.9344 - val_loss: 0.8744 - val_accuracy: 0.8074 Epoch 79/100 420/420 [==============================] - 28s 67ms/step - loss: 0.1700 - accuracy: 0.9462 - val_loss: 0.9048 - val_accuracy: 0.8036 Epoch 80/100 420/420 [==============================] - 26s 62ms/step - loss: 0.1768 - accuracy: 0.9436 - val_loss: 0.9634 - val_accuracy: 0.7891 Epoch 81/100 420/420 [==============================] - 28s 67ms/step - loss: 0.2082 - accuracy: 0.9350 - val_loss: 0.8553 - val_accuracy: 0.8189 Epoch 82/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1582 - accuracy: 0.9498 - val_loss: 0.9084 - val_accuracy: 0.8087 Epoch 83/100 420/420 [==============================] - 26s 63ms/step - loss: 0.1605 - accuracy: 0.9499 - val_loss: 0.9399 - val_accuracy: 0.7999 Epoch 84/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1603 - accuracy: 0.9490 - val_loss: 0.9406 - val_accuracy: 0.8018 Epoch 85/100 420/420 [==============================] - 26s 62ms/step - loss: 0.1504 - accuracy: 0.9522 - val_loss: 0.9129 - val_accuracy: 0.8050 Epoch 86/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1579 - accuracy: 0.9500 - val_loss: 1.2079 - val_accuracy: 0.7429 Epoch 87/100 420/420 [==============================] - 27s 64ms/step - loss: 0.1612 - accuracy: 0.9494 - val_loss: 0.9434 - val_accuracy: 0.7891 Epoch 88/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1365 - accuracy: 0.9562 - val_loss: 1.0027 - val_accuracy: 0.7953 Epoch 89/100 420/420 [==============================] - 27s 65ms/step - loss: 0.1706 - accuracy: 0.9480 - val_loss: 0.9476 - val_accuracy: 0.8123 Epoch 90/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1420 - accuracy: 0.9551 - val_loss: 0.9644 - val_accuracy: 0.8109 Epoch 91/100 420/420 [==============================] - 27s 64ms/step - loss: 0.1511 - accuracy: 0.9512 - val_loss: 0.9132 - val_accuracy: 0.8111 Epoch 92/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1585 - accuracy: 0.9498 - val_loss: 0.9786 - val_accuracy: 0.8061 Epoch 93/100 420/420 [==============================] - 27s 64ms/step - loss: 0.1287 - accuracy: 0.9583 - val_loss: 0.9915 - val_accuracy: 0.8041 Epoch 94/100 420/420 [==============================] - 27s 63ms/step - loss: 0.1384 - accuracy: 0.9566 - val_loss: 0.9377 - val_accuracy: 0.8132 Epoch 95/100 420/420 [==============================] - 30s 71ms/step - loss: 0.1186 - accuracy: 0.9617 - val_loss: 1.1608 - val_accuracy: 0.7610 Epoch 96/100 420/420 [==============================] - 27s 64ms/step - loss: 0.1375 - accuracy: 0.9561 - val_loss: 0.9701 - val_accuracy: 0.8122 Epoch 97/100 420/420 [==============================] - 28s 66ms/step - loss: 0.1323 - accuracy: 0.9585 - val_loss: 1.9326 - val_accuracy: 0.7048 Epoch 98/100 420/420 [==============================] - 27s 65ms/step - loss: 0.1134 - accuracy: 0.9634 - val_loss: 1.0359 - val_accuracy: 0.8123 Epoch 99/100 420/420 [==============================] - 27s 64ms/step - loss: 0.1320 - accuracy: 0.9573 - val_loss: 1.0012 - val_accuracy: 0.8091 Epoch 100/100 420/420 [==============================] - 27s 63ms/step - loss: 0.1161 - accuracy: 0.9635 - val_loss: 0.9669 - val_accuracy: 0.8193
MODEL_FIN_DF = pd.DataFrame.from_dict(HISTORY.history)
Y_PRED = MODEL_FIN.predict(X_TEST)
print('MODEL SCORE: ', metrics.r2_score(Y_TEST,Y_PRED))
print('\n\n',MODEL_FIN_DF.sort_values('accuracy',ascending = False).head())
563/563 [==============================] - 7s 12ms/step
MODEL SCORE: 0.6843910111728524
loss accuracy val_loss val_accuracy
99 0.116148 0.963524 0.966853 0.819278
97 0.113372 0.963405 1.035892 0.812278
94 0.118624 0.961714 1.160837 0.761000
96 0.132339 0.958452 1.932626 0.704778
92 0.128704 0.958333 0.991548 0.804111
INITIAL MODEL:
loss accuracy val_loss val_accuracy
97 0.177493 0.944548 0.996580 0.784722
95 0.186299 0.940595 0.939283 0.803778
99 0.191058 0.940214 0.948085 0.793222
93 0.194492 0.937738 1.065166 0.771833
94 0.194438 0.937690 0.999405 0.791389
FINAL MODEL:
loss accuracy val_loss val_accuracy
99 0.116148 0.963524 0.966853 0.819278
97 0.113372 0.963405 1.035892 0.812278
94 0.118624 0.961714 1.160837 0.761000
96 0.132339 0.958452 1.932626 0.704778
92 0.128704 0.958333 0.991548 0.804111
* WE CAN SEE IN THE FINAL MODEL, ACCURACY IS INCREASED IN BOTH TRAINING AND TESTING DATA
* AT MAXIMUM ACCURACY, WE CAN SEE THAT LOSS IN BOTH CASES WAS DECREASED.
* WE WILL PREDICT THE X AND THEN PRINT THE PREDICTIONS AND ACTUAL TEST IMAGE
PRED_X = MODEL_FIN.predict(X_TEST)
CLASS_X = np.argmax(PRED_X,axis=1)
CLASS_X
563/563 [==============================] - 7s 12ms/step
array([4, 7, 2, ..., 7, 9, 2], dtype=int64)
plt.imshow(X_TEST[5].reshape(32,32),cmap='gray')
print(CLASS_X[5])
9
plt.imshow(X_TEST[50].reshape(32,32),cmap='gray')
print(CLASS_X[50])
5
plt.imshow(X_TEST[150].reshape(32,32),cmap='gray')
print(CLASS_X[150])
3
plt.imshow(X_TEST[119].reshape(32,32),cmap='gray')
print(CLASS_X[119])
9
MODEL_FIN.summary()
Model: "sequential_19"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_118 (Dense) (None, 1024) 1049600
activation_42 (Activation) (None, 1024) 0
dense_119 (Dense) (None, 256) 262400
activation_43 (Activation) (None, 256) 0
dense_120 (Dense) (None, 512) 131584
activation_44 (Activation) (None, 512) 0
dense_121 (Dense) (None, 256) 131328
activation_45 (Activation) (None, 256) 0
dense_122 (Dense) (None, 128) 32896
activation_46 (Activation) (None, 128) 0
dense_123 (Dense) (None, 64) 8256
activation_47 (Activation) (None, 64) 0
dense_124 (Dense) (None, 128) 8320
leaky_re_lu_32 (LeakyReLU) (None, 128) 0
dense_125 (Dense) (None, 128) 16512
leaky_re_lu_33 (LeakyReLU) (None, 128) 0
dense_126 (Dense) (None, 128) 16512
leaky_re_lu_34 (LeakyReLU) (None, 128) 0
dense_127 (Dense) (None, 10) 1290
activation_48 (Activation) (None, 10) 0
=================================================================
Total params: 1,658,698
Trainable params: 1,658,698
Non-trainable params: 0
_________________________________________________________________
print('TRAINING SCORE USING FINAL MODEL: ', MODEL_FIN.evaluate(X_TRAIN, Y_TRAIN, verbose=0))
print('\n\nTESTING SCORE USING FINAL MODEL: ', MODEL_FIN.evaluate(X_TEST, Y_TEST, verbose=0))
TRAINING SCORE USING FINAL MODEL: [0.06799362599849701, 0.9801190495491028] TESTING SCORE USING FINAL MODEL: [0.9668534994125366, 0.8192777633666992]
print(MODEL_FIN_DF.describe())
loss accuracy val_loss val_accuracy count 100.000000 100.000000 100.000000 100.000000 mean 0.350767 0.886814 0.844765 0.785311 std 0.189227 0.061690 0.153564 0.027488 min 0.113372 0.721643 0.688915 0.671167 25% 0.203987 0.845619 0.758220 0.772028 50% 0.301088 0.902310 0.805437 0.793972 75% 0.477192 0.935131 0.881598 0.804917 max 0.843926 0.963524 1.932626 0.819278
* FINAL MODEL HAS PERFORMED WITH AVERAGE ACCURACY OF 88.68% FOR TRAINING DATA AND 78.53% FOR TESTING DATA
* AVERAGE LOSS OF THE MODEL 0.35 FOR TRAINING DATA AND 0.84 FOR TESTING DATA
* MAXIMUM ACCURACY ACHIEVED BY THE MODEL FOR TRAINING DATA IS 96.35%
* MAXIMUM ACCURACY ACHIEVED BY THE MODEL FOR TESTING DATA IS 81.92%
TRAINING_LOSS = MODEL_FIN_DF['loss']
VALIDATION_LOSS = MODEL_FIN_DF['val_loss']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_LOSS, 'r', label='TRAINING_LOSS')
plt.plot(EPOCHS, VALIDATION_LOSS, 'b', label='VALIDATION_LOSS')
plt.title('TRAINING AND VALIDATION LOSS COMPARISON VS NO OF EPOCHS\n')
plt.xlabel('EPOCHS')
plt.ylabel('LOSS')
plt.legend()
plt.show()
TRAINING_ACC = MODEL_FIN_DF['accuracy']
VALIDATION_ACC = MODEL_FIN_DF['val_accuracy']
EPOCHS = np.arange(1,101)
plt.plot(EPOCHS, TRAINING_ACC, 'r', label='TRAINING_ACCURACY')
plt.plot(EPOCHS, VALIDATION_ACC, 'b', label='VALIDATION_ACCURACY')
plt.title('TRAINING AND VALIDATION ACCURACY COMPARISON VS NO OF EPOCHS\n')
plt.xlabel('EPOCHS')
plt.ylabel('ACCURACY')
plt.legend()
plt.show()
MODEL_FIN_DF.plot(figsize=(10,10))
plt.show()
* TRAINING LOSS AND ACCURACY FOLLOW A PROPER CURVE UPWARD OR DOWNWARD.
* TRAINING LOSS IS DECREASING OVER NO OF EPOCHS INCREASED.
* TRAINING ACCURACY IS INCREASED AS THE NO OF EPOCHS INCREASED
* TESTING DATA ACCURACY, LOSS IS NOT A PROPER CURVE AND VARIES TO HIGH AND LOW AT DIFFERENT EPOCHS.
* WE HAVE USED DIFFERENT PARAMETERS AND TRAINED THE MODEL AND WE COULD SEE A SLIGHT IMPROVEMENT
**************END OF SOLUTION FOR NEURAL NETWORK PROBLEM STATEMENT*************